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ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id BB524131E96 for ; Tue, 1 Aug 2017 09:28:03 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,306,1498514400"; d="scan'208";a="233428808" Received: from static-176-183-206-74.ncc.abo.bbox.fr (HELO [192.168.1.27]) ([176.183.206.74]) by mail3-relais-sop.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 01 Aug 2017 18:28:01 +0200 To: yanshen References: <5D36713D8A4E7348A7E10DF7437A4B927CE3A673@NKGEML515-MBX.china.huawei.com> <20170727004512.GC18300@spectre> <6AE399511121AB42A34ACEF7BF25B4D2979B8A@DGGEMM505-MBS.china.huawei.com> Cc: "idnet@ietf.org" From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: <8ad08c34-2808-0341-8d6a-982c9f02ef17@inria.fr> Date: Tue, 1 Aug 2017 18:28:00 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D2979B8A@DGGEMM505-MBS.china.huawei.com> Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: 8bit Archived-At: Subject: Re: [Idnet] Architecture and standard opportunities of IDN X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 01 Aug 2017 16:28:06 -0000 Hi, Le 31/07/2017 à 14:25, yanshen a écrit : >> Le 27/07/2017 à 02:45, Pedro Martinez-Julia a écrit : >>> Dear Sheng, >>> >>> First of all, and considering my recent experience with the Network >>> Slicing work, I think we have some aspects of our work that are >>> probably essential for the IETF and network technology in general: >>> >>> - As you mention and all of us know well, we need to standardize the >>> data format, so it is a good opportunity to define some ontology and >>> the corresponding YANG model that structures such data. >> Do you imagine kind of a transformation process in the architecture as data to be >> used can come with pre-existing various data formats ? > My two cents. I ever think about that. The proposed data format should be easily "re-constructed" via existing data formats. The new attributes in the new format should can be auto-filled. Like the transformation of "XML schema" to "YANG". > >> So, listing "all" (the most used) data format (attributes and encoding) should be a starting point jerome From nobody Tue Aug 1 17:44:56 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 59AC6131C31 for ; Tue, 1 Aug 2017 17:44:54 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.463 X-Spam-Level: X-Spam-Status: No, score=-1.463 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_IMAGE_RATIO_02=0.437, HTML_MESSAGE=0.001, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=no autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id f_1ixw2XlcaX for ; Tue, 1 Aug 2017 17:44:52 -0700 (PDT) Received: from ms.etri.re.kr (mscreen.etri.re.kr [129.254.9.16]) by ietfa.amsl.com (Postfix) with ESMTP id 1D6CE131714 for ; Tue, 1 Aug 2017 17:44:50 -0700 (PDT) Received: from unknown (HELO smtpeg.etri.re.kr) (129.254.27.141) by 129.254.9.16 with ESMTP; 2 Aug 2017 09:44:57 +0900 X-Original-SENDERIP: 129.254.27.141 X-Original-MAILFROM: mskim16@etri.re.kr X-Original-RCPTTO: yanshen@huawei.com, idnet@ietf.org, jiangsheng@huawei.com Received: from SMTP5.etri.info (129.254.28.75) by SMTPEG1.etri.info (129.254.27.141) with Microsoft SMTP Server (TLS) id 14.3.319.2; Wed, 2 Aug 2017 09:44:47 +0900 Received: from SMTP1.etri.info ([169.254.1.45]) by SMTP5.etri.info ([129.254.28.75]) with mapi id 14.03.0319.002; Wed, 2 Aug 2017 09:44:45 +0900 From: =?utf-8?B?6rmA66+87ISd?= To: Sheng Jiang , "idnet@ietf.org" CC: yanshen Thread-Topic: Architecture and standard opportunities of IDN Thread-Index: AdMGA6fVfWTKGPKySXypIgnjpRC5HgDmjdFxAD+wjUAAIrLYKA== Date: Wed, 2 Aug 2017 00:44:45 +0000 Message-ID: <5BC916BD50F92F45870ABA46212CB29C019C4DDB@SMTP1.etri.info> References: <5D36713D8A4E7348A7E10DF7437A4B927CE3A673@NKGEML515-MBX.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29C019C145D@SMTP1.etri.info>, <5D36713D8A4E7348A7E10DF7437A4B927CE3D850@NKGEML515-MBX.china.huawei.com> In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CE3D850@NKGEML515-MBX.china.huawei.com> Accept-Language: ko-KR, en-US Content-Language: ko-KR X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [129.254.28.41] Content-Type: multipart/related; boundary="_004_5BC916BD50F92F45870ABA46212CB29C019C4DDBSMTP1etriinfo_"; type="multipart/alternative" MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] Architecture and standard opportunities of IDN X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 02 Aug 2017 00:44:54 -0000 --_004_5BC916BD50F92F45870ABA46212CB29C019C4DDBSMTP1etriinfo_ Content-Type: 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--_004_5BC916BD50F92F45870ABA46212CB29C019C4DDBSMTP1etriinfo_-- From nobody Wed Aug 2 03:12:35 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 237A9126BF0 for ; Wed, 2 Aug 2017 03:12:34 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.222 X-Spam-Level: X-Spam-Status: No, score=-4.222 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id qCGgcttjYB2T for ; Wed, 2 Aug 2017 03:12:32 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by 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Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-OriginatorOrg: nokia-bell-labs.com X-MS-Exchange-CrossTenant-originalarrivaltime: 02 Aug 2017 12:35:50.9669 (UTC) X-MS-Exchange-CrossTenant-fromentityheader: Hosted X-MS-Exchange-CrossTenant-id: 5d471751-9675-428d-917b-70f44f9630b0 X-MS-Exchange-Transport-CrossTenantHeadersStamped: HE1PR0701MB2603 Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 02 Aug 2017 12:35:57 -0000 SGVsbG8sDQoNCkhpbnQ6IGl0IHdvdWxkIGJlIG5pY2UgdG8gY29udGFjdCB0aGUgTk1SRyBjaGFp cnMgcmVnYXJkaW5nIHRoZSBkZWRpY2F0ZWQgc2Vzc2lvbiB5b3UgYXJlIG1lbnRpb25pbmcuDQpB bHNvLCBwYXJ0aWNpcGFudHMgb2YgdGhlIE5NUkcgY291bGQvc2hvdWxkIGJlIGludGVyZXN0ZWQv YXdhcmUgYW5kIGNvdWxkIGV2ZW50dWFsbHkgY29udHJpYnV0ZSB0byB0aGUgZWZmb3J0Lg0KDQpU 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(amavisd-new, port 10024) with ESMTP id r8MMdk_pDmTn for ; Wed, 2 Aug 2017 06:22:53 -0700 (PDT) Received: from mail-wr0-x22b.google.com (mail-wr0-x22b.google.com [IPv6:2a00:1450:400c:c0c::22b]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 2BEB8126BF3 for ; Wed, 2 Aug 2017 06:22:53 -0700 (PDT) Received: by mail-wr0-x22b.google.com with SMTP id 33so18675715wrz.4 for ; Wed, 02 Aug 2017 06:22:53 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:sender:in-reply-to:references:from:date:message-id :subject:to; bh=duI4ZLkLj3IvGDs6lwbBrolK1faVKLKmv4GgmwQ6k3k=; b=oi89GcA0UPUKwVUeqncNjI3LKHIEQyGL1laHWs4iVh2ysEaQYJDUjHBjkOcQu+X0/v ILFfv+uStF60WSCUZKKMHlaHiEb7JnJk3UZ/eV5ydOWABv+HxnovIIEZX+Huu9/I+SN0 nSpI/Q/AjK+X4bFOwKyAyA+2lV2xRJWVYdVWPItCUj/pFav+gxqL5lp/TnOxQvjucVzX aLkO4Ph578S/n5+ixpiyGvIQdDOz0dhcNtkBoC562zsab06oAjEAGmQ3Dq7y1OFB2ox+ 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-0700 (PDT) In-Reply-To: References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> From: Aydin Ulas Date: Wed, 2 Aug 2017 16:22:10 +0300 X-Google-Sender-Auth: SIpmfVHF9HkgibDZgJhSYVKLvVU Message-ID: To: yanshen , "idnet@ietf.org" Content-Type: multipart/alternative; boundary="001a114741c82cc8dc0555c5295b" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 02 Aug 2017 13:22:55 -0000 --001a114741c82cc8dc0555c5295b Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hi, Here is my suggestion. Use case N+1: Application (and/or DDoS) detection Description: Detect the application (or attack) from network packets (HTTPS or plain) Collect the history traffic data and identify a service or attack (ex: Skype, Viber, DDoS attack etc.) Process: 1. Data collection (e.g. traffic sample of application to be detected or attack dump); 2. Training Model (includes data correlation from different parts of the network); 3. Real-time data capture and input; 4. Output prediction; 5. Online or periodically updating model parameters according to changes in the application (or attack style) Data Format: Time: [Timestamp, ...] Data Packet: [Packet Header, optional: Packet Content or the first few bytes: src/dst ip, src/dst port, frame length/number, ...] Direction: IN / OUT Route : [R1, R2, ..., RN] Traffic: [T0, T1, T2, ..., TN] Message : Request: ask for the data Reply: Identification of application Notice: For notification or others Policy: Control policy Best regards, Aydin On Wed, Aug 2, 2017 at 3:35 PM, Ciavaglia, Laurent (Nokia - FR/Nozay) < laurent.ciavaglia@nokia-bell-labs.com> wrote: > Hello, > > Hint: it would be nice to contact the NMRG chairs regarding the dedicated > session you are mentioning. > Also, participants of the NMRG could/should be interested/aware and could > eventually contribute to the effort. > > Thanks, Laurent (as NMRG co-chair). > --- > > -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen > Sent: Wednesday, August 02, 2017 12:12 PM > To: idnet@ietf.org > Subject: [Idnet] IDN dedicated session call for case > > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, for > applying AI into network management (NM), I=E2=80=99d try to list some Us= e Cases > and propose a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose your > focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external data > which may influence the traffic. Predict the traffic in short/long/specif= ic > term. Avoid the congestion or risk in previously. > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data capture and > input; 4. Predication output; 5. Fix error and go back to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might be > useful for some scenarios) > Service : [Service ID, Priority, ...] > (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic flows. > Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of physical/logical > port ); 2. Training Model; 3. Real-time data capture and input; 4. Output > percentages; 5. Fix error and go back to 3. > Data Format: Time : [Timestamp, Value type (Delay/Packet > Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope before Nov. > Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a114741c82cc8dc0555c5295b Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi,
Here is my suggestion.

Use case N+1: Application (and/or DDoS) dete= ction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Detect the application (or attack= ) from network packets (HTTPS or plain) Collect the history traffic data an= d identify a service or attack (ex: Skype, Viber, DDoS attack etc.)<= br style=3D"font-size:12.8px">=C2=A0 =C2= =A0 =C2=A0 =C2=A0 Process: 1. Data collection (e.g. traffic sample of appli= cation to be detected or attack dump); 2. Training Model (includes=C2=A0data correlation from different parts = of the network); 3. Real-time data = capture and input; 4. Output prediction; 5. Online or periodically updating= model parameters according to changes in the application (or attack style)= =C2=A0
=C2=A0 =C2=A0 =C2= =A0 =C2=A0 Data Format: =C2=A0 Time:=C2=A0[Timestamp, ...]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Packet: [Packet Header, optional: Packet Content = or the first few bytes: src/dst ip, src/dst port, frame length/number, ...<= /span>]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 D= irection: IN / OUT
=C2=A0= =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Route : [R1, R2, ..., RN]=C2=A0
=C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 Traffic: [T0, T1, T2, ..., TN]

=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 = =C2=A0 =C2=A0 =C2=A0Request: ask for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0= =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Reply: Identification of application
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 N= otice: For notification or others
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: = Control policy


Best rega= rds,
Aydin

On Wed, Aug 2, 2017 at 3:35 PM, Ciavaglia, Laurent (Nokia - FR/Noz= ay) <laurent.ciavaglia@nokia-bell-labs.com>= ; wrote:
Hello,=

Hint: it would be nice to contact the NMRG chairs regarding the dedicated s= ession you are mentioning.
Also, participants of the NMRG could/should be interested/aware and could e= ventually contribute to the effort.

Thanks, Laurent (as NMRG co-chair).
---

-----Original Message-----
From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Wednesday, August 02, 2017 12:12 PM
To: idnet@ietf.org<= br> Subject: [Idnet] IDN dedicated session call for case

Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applyin= g AI into network management (NM), I=E2=80=99d try to list some Use Cases a= nd propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focus= ed use cases or ideas.

Use case 1: Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Collect the history traffic data a= nd external data which may influence the traffic. Predict the traffic in sh= ort/long/specific term. Avoid the congestion or risk in previously.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data collection (e.g. traffic sampl= e of physical/logical port ); 2. Training Model; 3. Real-time data capture = and input; 4. Predication output; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Start, End, U= nit, Number of Value, Sampling Period]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Device ID, Port ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Direction: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Route : [R1, R2, ..., RN]=C2=A0 (mig= ht be useful for some scenarios)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Service : [Service ID, Priority, ...= ]=C2=A0 (Not clear how to use it but seems useful)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Traffic: [T0, T1, T2, ..., TN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case 2: QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Use multiple paths to distribute t= he traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.<= br> =C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data capture (e.g. traffic sample o= f physical/logical port ); 2. Training Model; 3. Real-time data capture and= input; 4. Output percentages; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Timestamp, Va= lue type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Link ID, Device ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Value: [V0, V1, V2, ..., VN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requi= rements Sep. : Refining the cases and abstract the common elements Oct. : D= eeply analysis. Especially on Data Format, control flow, or other key point= s
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. H= ope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.1 Network Management requirement
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.2 TBD
2. Use Cases
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.1 Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.2 QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
3. Data Focus
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.1 Data attribute
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.2 Data format
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
4. Aims
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.1 Benchmarking Framework
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.2 TBD
###ToC End###


Yansen

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dGYub3JnL21haWxtYW4vbGlzdGluZm8vaWRuZXQ8L2E+PG86cD48L286cD48L3A+DQo8L2Rpdj4N CjwvZGl2Pg0KPC9ibG9ja3F1b3RlPg0KPC9kaXY+DQo8cCBjbGFzcz0iTXNvTm9ybWFsIj48bzpw PiZuYnNwOzwvbzpwPjwvcD4NCjwvZGl2Pg0KPC9kaXY+DQo8L2Rpdj4NCjwvYm9keT4NCjwvaHRt bD4NCg== --_000_6AE399511121AB42A34ACEF7BF25B4D297A4E5DGGEMM505MBSchina_-- From nobody Wed Aug 2 17:35:30 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E3E46127601 for ; Wed, 2 Aug 2017 17:35:28 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.202 X-Spam-Level: X-Spam-Status: No, score=-4.202 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id pRyJ5M-0fDGr for ; Wed, 2 Aug 2017 17:35:27 -0700 (PDT) Received: from ns1.nict.go.jp (ns1.nict.go.jp [IPv6:2001:df0:232:300::1]) by ietfa.amsl.com (Postfix) with ESMTP id 33C60126C7A for ; Wed, 2 Aug 2017 17:35:27 -0700 (PDT) Received: from gw1.nict.go.jp (gw1.nict.go.jp [133.243.18.250]) by ns1.nict.go.jp with ESMTP id v730ZI7l009910; Thu, 3 Aug 2017 09:35:18 +0900 (JST) Received: from mail2.nict.go.jp (mail2.nict.go.jp [133.243.18.15]) by gw1.nict.go.jp with ESMTP id v730ZIxJ009838; Thu, 3 Aug 2017 09:35:18 +0900 (JST) Received: from spectre (unknown [133.243.115.68]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail2.nict.go.jp (NICT Mail Spool Server2) with ESMTPS id AD8EAC242; Thu, 3 Aug 2017 09:35:18 +0900 (JST) Date: Thu, 3 Aug 2017 09:35:18 +0900 From: Pedro Martinez-Julia To: "Ciavaglia, Laurent (Nokia - FR/Nozay)" Cc: yanshen , "idnet@ietf.org" , "nmrg-chairs@irtf.org" , Lisandro Granville Message-ID: <20170803003518.GP18300@spectre> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.3 (2017-05-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith1 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 03 Aug 2017 00:35:29 -0000 On Wed, Aug 02, 2017 at 12:35:50PM +0000, Ciavaglia, Laurent (Nokia - FR/Nozay) wrote: > Hello, > > Hint: it would be nice to contact the NMRG chairs regarding the > dedicated session you are mentioning. > > Also, participants of the NMRG could/should be interested/aware and > could eventually contribute to the effort. > > Thanks, Laurent (as NMRG co-chair). Dear Laurent, I think there is a confusion regarding the "dedicated session". It is not scheduled or planned just it is a "wish". First we were discussing to do some work, possibly to write a document (as mentioned), and then try to find a session to fit it, which would potentially be NMRG. There is no content so we have still not asked for any time-slot but we would probably ask for it. Would you please intercede in our name to ask the chairs (yourself included) if NMRG can host this work? Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Mon Aug 7 18:23:25 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 9AFAD124207 for ; Mon, 7 Aug 2017 18:23:16 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=unavailable autolearn_force=no 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09:23:03 +0800 From: Sheng Jiang To: yanshen , "idnet@ietf.org" , "nmrg@irtf.org" Thread-Topic: IDN dedicated session call for case Thread-Index: AdMLcu+vuWBrdNuZQwG6l2oQPpJcKAEb70gw Date: Tue, 8 Aug 2017 01:23:03 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CE408E8@NKGEML515-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> Accept-Language: en-GB, zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.111.185.119] Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020206.59891281.00A4, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=0.0.0.0, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: fa10d073f6b88e98bcce9a64cf28278c Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 01:23:16 -0000 SGkgSURuZXRlciwgZXNwZWNpYWxseSB0aGUgY29udHJpYnV0b3JzLCANCg0KKGNvcGllZCB0byBO TVJHIG1haWwgbGlzdCBmb3IgY29vcmRpbmF0aW9uIGJldHdlZW4gdHdvIGdyb3VwcykNCg0KSXQg aXMgb3VyIGludGVudGlvbiB0byBvcmdhbml6ZSBhIGRlZGljYXRlZCBJRE5ldCBzZXNzaW9uIGZv ciBhcHBseWluZyBBSSBpbnRvIG5ldHdvcmsgbWFuYWdlbWVudCBpbiBOTVJHIGluIElFVEYxMDAs IFNpbmdhcG9yZS4gSXQgd2lsbCBkZXBlbmQgb24gd2hldGhlciB3ZSBjb3VsZCBjb252ZXJnZSBh bmQgb3JnYW5pemUgZ29vZCBjb250ZW50L3ByZXNlbnRhdGlvbnMuIFNvIGZhciwgd2UgYXJlIGFj dHVhbGx5IGluIHRoZSBzdGFnZSBvZiBjYWxsaW5nIGludGVyZXN0cywgdXNlIGNhc2VzIGFuZCBj b250cmlidXRvcnMuIFdlIG5lZWQgdG8ga2VlcCBkaXNjdXNzaW5nIGFuZCBjb252ZXJnZSB0byBz 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<6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> From: Albert Cabellos Date: Tue, 8 Aug 2017 13:52:23 +0900 Message-ID: To: yanshen Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary="001a114a802eb1791e055636baeb" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 04:52:27 -0000 --001a114a802eb1791e055636baeb Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hi all Here=C2=B4s another use-case: Use case N+2: QoE Description: Collect low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then use ML to understand what is the relation between satisfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M wrote: > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, for > applying AI into network management (NM), I=E2=80=99d try to list some Us= e Cases > and propose a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose your > focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external data > which may influence the traffic. Predict the traffic in short/long/specif= ic > term. Avoid the congestion or risk in previously. > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data capture and > input; 4. Predication output; 5. Fix error and go back to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might be > useful for some scenarios) > Service : [Service ID, Priority, ...] > (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic flows. > Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of physical/logical > port ); 2. Training Model; 3. Real-time data capture and input; 4. Output > percentages; 5. Fix error and go back to 3. > Data Format: Time : [Timestamp, Value type (Delay/Packet > Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope before Nov. > Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a114a802eb1791e055636baeb Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi all

Here=C2=B4s another use-case:

Use case N+2: QoE
=C2=A0= =C2=A0 =C2=A0 =C2=A0 Description: Collect low-level metrics (SNR, latency,= jitter, losses, etc) and measure QoE. Then use ML to understand what is th= e relation between satisfactory QoE and the low-level metrics. As an exampl= e learn that when delay>N then QoE is degraded, but when M<delay<N= then QoE is satisfactory for the customers (please note that QoE cannot be= measured directly over your network). This is useful to understand how the= network must be operated to provide satisfactory QoE.
=C2=A0 =C2=A0 =C2=A0 =C2= =A0 Process: 1. Low-level data collection and QoE measurement ; 2. Training= Model (input low-level metrics, output QoE); 3. Real-time data capture and= input; 4. Predict QoE; 5. Operate network to meet target QoE requirement, = go to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Start, En= d, Unit, Number of Value, Sampling Period]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Position: [Device ID, Port ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Directio= n: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Low-level metric : SNR, Delay= , Jitter, queue-size, etc

<= span style=3D"font-size:12.8px">=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0= =C2=A0 =C2=A0 =C2=A0Request: ask for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notificati= on or others
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy=

Kind regards

Albert<= /div>

On Wed= , Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrot= e:
Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applyin= g AI into network management (NM), I=E2=80=99d try to list some Use Cases a= nd propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focus= ed use cases or ideas.

Use case 1: Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Collect the history traffic data a= nd external data which may influence the traffic. Predict the traffic in sh= ort/long/specific term. Avoid the congestion or risk in previously.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data collection (e.g. traffic sampl= e of physical/logical port ); 2. Training Model; 3. Real-time data capture = and input; 4. Predication output; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Start, End, U= nit, Number of Value, Sampling Period]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Device ID, Port ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Direction: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Route : [R1, R2, ..., RN]=C2=A0 (mig= ht be useful for some scenarios)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Service : [Service ID, Priority, ...= ]=C2=A0 (Not clear how to use it but seems useful)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Traffic: [T0, T1, T2, ..., TN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case 2: QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Use multiple paths to distribute t= he traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.<= br> =C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data capture (e.g. traffic sample o= f physical/logical port ); 2. Training Model; 3. Real-time data capture and= input; 4. Output percentages; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Timestamp, Va= lue type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Link ID, Device ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Value: [V0, V1, V2, ..., VN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requi= rements
Sep. : Refining the cases and abstract the common elements
Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. H= ope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.1 Network Management requirement
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.2 TBD
2. Use Cases
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.1 Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.2 QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
3. Data Focus
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.1 Data attribute
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.2 Data format
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
4. Aims
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.1 Benchmarking Framework
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.2 TBD
###ToC End###


Yansen

_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

--001a114a802eb1791e055636baeb-- From nobody Mon Aug 7 23:01:59 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id C6A84127136 for ; Mon, 7 Aug 2017 23:01:57 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=simula-no.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id iG-iRQyNOOtS for ; Mon, 7 Aug 2017 23:01:54 -0700 (PDT) Received: from mail-ua0-x234.google.com (mail-ua0-x234.google.com [IPv6:2607:f8b0:400c:c08::234]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 4DA5A127010 for ; Mon, 7 Aug 2017 23:01:54 -0700 (PDT) Received: by mail-ua0-x234.google.com with SMTP id 80so10591930uas.0 for ; Mon, 07 Aug 2017 23:01:54 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=simula-no.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=a524XUPaCO8EOn1rzsqRub0u1xv/BQKQ73wGE+aHjsg=; b=2GJbMDqDdCzjmLbq9cyZcGLmZtbyDnbv946thkQGZLIzGXstLEAxTcUGjesceOxH66 kfiCuboJiCmS/KPcyhg6Rr5J0pOHbDMVGZ5gmkgcgGIl+EOrZ2uTHqboKJn0eCw+O9iV wYIA4/ek2zIER+E/49jF02VXxaMiPj6Nwp6C2jcSMiexeCX8wFC95ZT1Tifr1a7DLSP0 9fQzEgsdRubM6lEpYi6uu6RkHgQDRovDXvNvlJu3yWbUzbEIFrAbD+LIBylQcOhaJDea JslajdFxZTqb+rC2rxrbJihVGlaYLCtI35I6GLwaq8L6Kz73qMiETSYBLHE0i2BvhfUw N/TA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=a524XUPaCO8EOn1rzsqRub0u1xv/BQKQ73wGE+aHjsg=; b=HCLr38HhZZ1u+l3aoubABD5vewOcWORBfaNAyhVwiqq2GLA7FLVTc0TzVdBLJngsZM u3mn1sApQP8M87Wk5w4Z9r07x4r76+20vqL9m61QZLMEgFngQPyyU7O4FxiTG58WJCSJ k8AXu7pDry63e5JZhXAJd8tVPYHkfPBUTbGbToTpbi2gfoVvJ4Y8pO2ruVMTez1FgesN EMd/EFi9FjItSYnDVyJak+oafDd4vkso18b+Prt+nbCZPoqQbZvvgXSI3C4jnSKvNqL3 OIxItuaxRvnIvdiDb31Y9Jrnmt/riRgOevdH9IfQFdLq/dZ6rbGNMcRB7lXnANvCijvi yoOA== X-Gm-Message-State: AHYfb5iAHBj5UrswED5uFbb8TG3bOPp76AVQ7Pex4Q1ptgwxGQw0pJOm /D5GlqtR0iPzvANuRlsapxNwsSnHghaY X-Received: by 10.159.52.79 with SMTP id s15mr2263754uab.157.1502172113230; Mon, 07 Aug 2017 23:01:53 -0700 (PDT) MIME-Version: 1.0 Received: by 10.31.107.217 with HTTP; Mon, 7 Aug 2017 23:01:32 -0700 (PDT) In-Reply-To: References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> From: =?UTF-8?B?w5Z6Z8O8IEFsYXk=?= Date: Tue, 8 Aug 2017 08:01:32 +0200 Message-ID: To: Albert Cabellos Cc: yanshen , "idnet@ietf.org" Content-Type: multipart/alternative; boundary="f403043ed1d43370ec055637b361" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 06:01:58 -0000 --f403043ed1d43370ec055637b361 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Dear Albert, We are interested in this use case and will support the activities in this area. Best Regards, =C3=96zg=C3=BC On 8 August 2017 at 06:52, Albert Cabellos wrote: > Hi all > > Here=C2=B4s another use-case: > > Use case N+2: QoE > Description: Collect low-level metrics (SNR, latency, jitter, > losses, etc) and measure QoE. Then use ML to understand what is the > relation between satisfactory QoE and the low-level metrics. As an exampl= e > learn that when delay>N then QoE is degraded, but when M is satisfactory for the customers (please note that QoE cannot be measure= d > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > Process: 1. Low-level data collection and QoE measurement ; 2. > Training Model (input low-level metrics, output QoE); 3. Real-time data > capture and input; 4. Predict QoE; 5. Operate network to meet target QoE > requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, Jitter, > queue-size, etc > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Kind regards > > Albert > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen wrote: > >> Dear all, >> >> Since we plan to organize a dedicated session in NMRG, IETF100, for >> applying AI into network management (NM), I=E2=80=99d try to list some U= se Cases >> and propose a roadmap and ToC before Nov. >> >> These might be rough. You are welcome to refine them and propose your >> focused use cases or ideas. >> >> Use case 1: Traffic Prediction >> Description: Collect the history traffic data and external data >> which may influence the traffic. Predict the traffic in short/long/speci= fic >> term. Avoid the congestion or risk in previously. >> Process: 1. Data collection (e.g. traffic sample of >> physical/logical port ); 2. Training Model; 3. Real-time data capture an= d >> input; 4. Predication output; 5. Fix error and go back to 3. >> Data Format: Time : [Start, End, Unit, Number of Value, >> Sampling Period] >> Position: [Device ID, Port ID] >> Direction: IN / OUT >> Route : [R1, R2, ..., RN] (might be >> useful for some scenarios) >> Service : [Service ID, Priority, ...] >> (Not clear how to use it but seems useful) >> Traffic: [T0, T1, T2, ..., TN] >> Message : Request: ask for the data >> Reply: Data >> Notice: For notification or others >> Policy: Control policy >> >> Use case 2: QoS Management >> Description: Use multiple paths to distribute the traffic flows. >> Adjust the percentages. Avoid congestion and ensure QoS. >> Process: 1. Data capture (e.g. traffic sample of physical/logica= l >> port ); 2. Training Model; 3. Real-time data capture and input; 4. Outpu= t >> percentages; 5. Fix error and go back to 3. >> Data Format: Time : [Timestamp, Value type (Delay/Packet >> Loss/...), Unit, Number of Value, Sampling Period] >> Position: [Link ID, Device ID] >> Value: [V0, V1, V2, ..., VN] >> Message : Request: ask for the data >> Reply: Data >> Notice: For notification or others >> Policy: Control policy >> >> Use case N: Waiting for your Ideas >> >> Also I suggest a roadmap before Nov if possible. >> >> ### Roadmap ### >> Aug. : Collecting the use cases (related with NM). Rough thoughts and >> requirements >> Sep. : Refining the cases and abstract the common elements >> Oct. : Deeply analysis. Especially on Data Format, control flow, or othe= r >> key points >> Nov.: F2F discussions on IETF100 >> ### Roadmap End ### >> >> A rough ToC is listed in following. We may take it as a scope before Nov= . >> Hope that the content could become the draft of draft. >> >> ###Table of Content### >> 1. Gap and Requirement Analysis >> 1.1 Network Management requirement >> 1.2 TBD >> 2. Use Cases >> 2.1 Traffic Prediction >> 2.2 QoS Management >> 3.3 TBD >> 3. Data Focus >> 3.1 Data attribute >> 3.2 Data format >> 3.3 TBD >> 4. Aims >> 4.1 Benchmarking Framework >> 4.2 TBD >> ###ToC End### >> >> >> Yansen >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --f403043ed1d43370ec055637b361 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Dear Albert,=C2=A0
We are interested in this use case = and will support the activities in this area.
Best Regards,
=
=C3=96zg=C3=BC

On 8 August 2017 at 06:52, Albert Cabellos <= span dir=3D"ltr"><albert.cabellos@gmail.com> wrote:
Hi all

Here=C2=B4s anot= her use-case:

Use= case N+2: QoE
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Collect low-level metric= s (SNR, latency, jitter, losses, etc) and measure QoE. Then use ML to under= stand what is the relation between satisfactory QoE and the low-level metri= cs. As an example learn that when delay>N then QoE is degraded, but when= M<delay<N then QoE is satisfactory for the customers (please note th= at QoE cannot be measured directly over your network). This is useful to un= derstand how the network must be operated to provide satisfactory QoE.
=C2=A0 = =C2=A0 =C2=A0 =C2=A0 Process: 1. Low-level data collection and QoE measurem= ent ; 2. Training Model (input low-level metrics, output QoE); 3. Real-time= data capture and input; 4. Predict QoE; 5. Operate network to meet target = QoE requirement, go to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Form= at:=C2=A0 =C2=A0 Time : [Start, End, Unit, Number of Value, Sampling Period= ]
=C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Device ID, Port ID]=C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 Direction: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 Low-level metric : SNR, Delay, Jitter, queue-size, etc

=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2= =A0Request: ask for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data<= /span>
=C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others
=C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 = =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy
Kind regards

Albert

On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com&= gt; wrote:
Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applyin= g AI into network management (NM), I=E2=80=99d try to list some Use Cases a= nd propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focus= ed use cases or ideas.

Use case 1: Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Collect the history traffic data a= nd external data which may influence the traffic. Predict the traffic in sh= ort/long/specific term. Avoid the congestion or risk in previously.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data collection (e.g. traffic sampl= e of physical/logical port ); 2. Training Model; 3. Real-time data capture = and input; 4. Predication output; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Start, End, U= nit, Number of Value, Sampling Period]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Device ID, Port ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Direction: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Route : [R1, R2, ..., RN]=C2=A0 (mig= ht be useful for some scenarios)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Service : [Service ID, Priority, ...= ]=C2=A0 (Not clear how to use it but seems useful)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Traffic: [T0, T1, T2, ..., TN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case 2: QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Use multiple paths to distribute t= he traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.<= br> =C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data capture (e.g. traffic sample o= f physical/logical port ); 2. Training Model; 3. Real-time data capture and= input; 4. Output percentages; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Timestamp, Va= lue type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Link ID, Device ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Value: [V0, V1, V2, ..., VN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requi= rements
Sep. : Refining the cases and abstract the common elements
Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. H= ope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.1 Network Management requirement
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.2 TBD
2. Use Cases
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.1 Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.2 QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
3. Data Focus
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.1 Data attribute
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.2 Data format
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
4. Aims
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.1 Benchmarking Framework
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.2 TBD
###ToC End###


Yansen

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IGhyZWY9Im1haWx0bzpJRE5FVEBpZXRmLm9yZyI+SURORVRAaWV0Zi5vcmc8L2E+PGJyPg0KPGEg aHJlZj0iaHR0cHM6Ly93d3cuaWV0Zi5vcmcvbWFpbG1hbi9saXN0aW5mby9pZG5ldCIgdGFyZ2V0 PSJfYmxhbmsiPmh0dHBzOi8vd3d3LmlldGYub3JnL21haWxtYW4vbGlzdGluZm8vaWRuZXQ8L2E+ PG86cD48L286cD48L3NwYW4+PC9wPg0KPC9ibG9ja3F1b3RlPg0KPC9kaXY+DQo8cCBjbGFzcz0i TXNvTm9ybWFsIj48c3BhbiBsYW5nPSJFTi1VUyI+PG86cD4mbmJzcDs8L286cD48L3NwYW4+PC9w Pg0KPC9kaXY+DQo8L2Rpdj4NCjwvZGl2Pg0KPC9kaXY+DQo8L2JvZHk+DQo8L2h0bWw+DQo= --_000_6AE399511121AB42A34ACEF7BF25B4D297B223DGGEMM505MBSchina_-- From nobody Tue Aug 8 07:40:18 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id D10F9132545 for ; Tue, 8 Aug 2017 07:40:16 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.898 X-Spam-Level: X-Spam-Status: No, score=-6.898 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-5, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id zxCrfSCKcret for ; Tue, 8 Aug 2017 07:40:14 -0700 (PDT) Received: from mail2-relais-roc.national.inria.fr (mail2-relais-roc.national.inria.fr [192.134.164.83]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5426913253D for ; Tue, 8 Aug 2017 07:40:13 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,343,1498514400"; d="scan'208,217";a="286473824" Received: from unknown (HELO [192.168.1.108]) ([89.130.180.250]) by mail2-relais-roc.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 08 Aug 2017 16:40:10 +0200 To: Aydin Ulas , yanshen , "idnet@ietf.org" References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: <2af1096e-6cb9-7a69-3373-be3b70099bea@inria.fr> Date: Tue, 8 Aug 2017 16:40:08 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: Content-Type: multipart/alternative; boundary="------------21734C1A2863F82BAF4DCCF0" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 14:40:17 -0000 This is a multi-part message in MIME format. --------------21734C1A2863F82BAF4DCCF0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: 8bit Hi, I support this use case and suggest to also include DDoS mitigation as part of the process, e.g. re-routing to DPI middleboxes / blocking traffic. You can thus evaluate the result of your mitigation decision and readjust your model accordingly with reinforcement learning. We have some on-going work in this area. Best regards, jerome Le 02/08/2017 à 15:22, Aydin Ulas a écrit : > Hi, > Here is my suggestion. > > Use case N+1: Application (and/or DDoS) detection > Description: Detect the application (or attack) from network > packets (HTTPS or plain) Collect the history traffic data and identify > a service or attack (ex: Skype, Viber, DDoS attack etc.) > Process: 1. Data collection (e.g. traffic sample of > application to be detected or attack dump); 2. Training Model > (includes data correlation from different parts of the network); 3. > Real-time data capture and input; 4. Output prediction; 5. Online or > periodically updating model parameters according to changes in the > application (or attack style) > Data Format: Time: [Timestamp, ...] > Data Packet: [Packet Header, optional: > Packet Content or the first few bytes: src/dst ip, src/dst port, frame > length/number, ...] > Direction: IN / OUT > Route : [R1, R2, ..., RN] > Traffic: [T0, T1, T2, ..., TN] > > Message : Request: ask for the data > Reply: Identification of application > Notice: For notification or others > Policy: Control policy > > > Best regards, > Aydin > > On Wed, Aug 2, 2017 at 3:35 PM, Ciavaglia, Laurent (Nokia - FR/Nozay) > > wrote: > > Hello, > > Hint: it would be nice to contact the NMRG chairs regarding the > dedicated session you are mentioning. > Also, participants of the NMRG could/should be interested/aware > and could eventually contribute to the effort. > > Thanks, Laurent (as NMRG co-chair). > --- > > -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org > ] On Behalf Of yanshen > Sent: Wednesday, August 02, 2017 12:12 PM > To: idnet@ietf.org > Subject: [Idnet] IDN dedicated session call for case > > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, > for applying AI into network management (NM), I’d try to list some > Use Cases and propose a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose > your focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external > data which may influence the traffic. Predict the traffic in > short/long/specific term. Avoid the congestion or risk in previously. > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Predication output; 5. Fix error and go back > to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might > be useful for some scenarios) > Service : [Service ID, Priority, > ...] (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic > flows. Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Output percentages; 5. Fix error and go back > to 3. > Data Format: Time : [Timestamp, Value type > (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts > and requirements Sep. : Refining the cases and abstract the common > elements Oct. : Deeply analysis. Especially on Data Format, > control flow, or other key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope > before Nov. Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --------------21734C1A2863F82BAF4DCCF0 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: 8bit Hi,

I support this use case and suggest to also include DDoS mitigation as part of the process, e.g. re-routing to DPI middleboxes / blocking traffic. You can thus evaluate the result of your mitigation decision and readjust your model accordingly with reinforcement learning.
We have some on-going work in this area.

Best regards,
jerome


Le 02/08/2017 à 15:22, Aydin Ulas a écrit :
Hi,
Here is my suggestion.

Use case N+1: Application (and/or DDoS) detection
        Description: Detect the application (or attack) from network packets (HTTPS or plain) Collect the history traffic data and identify a service or attack (ex: Skype, Viber, DDoS attack etc.)
        Process: 1. Data collection (e.g. traffic sample of application to be detected or attack dump); 2. Training Model (includes data correlation from different parts of the network); 3. Real-time data capture and input; 4. Output prediction; 5. Online or periodically updating model parameters according to changes in the application (or attack style) 
        Data Format:   Time: [Timestamp, ...]
                                Data Packet: [Packet Header, optional: Packet Content or the first few bytes: src/dst ip, src/dst port, frame length/number, ...]
                                Direction: IN / OUT
                                Route : [R1, R2, ..., RN] 
                                Traffic: [T0, T1, T2, ..., TN]

        Message :       Request: ask for the data
                                Reply: Identification of application
                                Notice: For notification or others
                                Policy: Control policy


Best regards,
Aydin

On Wed, Aug 2, 2017 at 3:35 PM, Ciavaglia, Laurent (Nokia - FR/Nozay) <laurent.ciavaglia@nokia-bell-labs.com> wrote:
Hello,

Hint: it would be nice to contact the NMRG chairs regarding the dedicated session you are mentioning.
Also, participants of the NMRG could/should be interested/aware and could eventually contribute to the effort.

Thanks, Laurent (as NMRG co-chair).
---

-----Original Message-----
From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Wednesday, August 02, 2017 12:12 PM
To: idnet@ietf.org
Subject: [Idnet] IDN dedicated session call for case

Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applying AI into network management (NM), I’d try to list some Use Cases and propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focused use cases or ideas.

Use case 1: Traffic Prediction
        Description: Collect the history traffic data and external data which may influence the traffic. Predict the traffic in short/long/specific term. Avoid the congestion or risk in previously.
        Process: 1. Data collection (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Predication output; 5. Fix error and go back to 3.
        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Route : [R1, R2, ..., RN]  (might be useful for some scenarios)
                                Service : [Service ID, Priority, ...]  (Not clear how to use it but seems useful)
                                Traffic: [T0, T1, T2, ..., TN]
        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Use case 2: QoS Management
        Description: Use multiple paths to distribute the traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.
        Process: 1. Data capture (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Output percentages; 5. Fix error and go back to 3.
        Data Format:    Time : [Timestamp, Value type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period]
                                Position: [Link ID, Device ID]
                                Value: [V0, V1, V2, ..., VN]
        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other key points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. Hope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
        1.1 Network Management requirement
        1.2 TBD
2. Use Cases
        2.1 Traffic Prediction
        2.2 QoS Management
        3.3 TBD
3. Data Focus
        3.1 Data attribute
        3.2 Data format
        3.3 TBD
4. Aims
        4.1 Benchmarking Framework
        4.2 TBD
###ToC End###


Yansen

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--------------21734C1A2863F82BAF4DCCF0-- From nobody Tue Aug 8 07:49:41 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 16E0413237F for ; Tue, 8 Aug 2017 07:49:40 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.898 X-Spam-Level: X-Spam-Status: No, score=-6.898 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-5, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id dU3lY3RgMX2R for ; Tue, 8 Aug 2017 07:49:37 -0700 (PDT) Received: from mail2-relais-roc.national.inria.fr (mail2-relais-roc.national.inria.fr [192.134.164.83]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id C2DD51321CD for ; Tue, 8 Aug 2017 07:49:36 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,343,1498514400"; d="scan'208,217";a="286474918" Received: from unknown (HELO [192.168.1.108]) ([89.130.180.250]) by mail2-relais-roc.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 08 Aug 2017 16:49:34 +0200 To: Albert Cabellos , yanshen References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> Cc: "idnet@ietf.org" From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> Date: Tue, 8 Aug 2017 16:49:32 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: Content-Type: multipart/alternative; boundary="------------7DCE67CEE91FD93F0D9FA92C" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 14:49:40 -0000 This is a multi-part message in MIME format. --------------7DCE67CEE91FD93F0D9FA92C Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Hi all, Here is another use case about traffic classification. Use case N+3: (encrypted) traffic classification Description: collect flow-level traffic metrics such as protocol information but also meta metrics such as distribution of packet sizes, inter-arrival times... Then use such information to label the trafic with the underlying application assuming that the granularity of classification may vary (type of application, exact application name, version...) Process: 1. collect packet information 2. flow reassembly (using directly flow format such as IPFIX might be possible but depends on the type of traffic, e.g. extracting the TLS application data is useful for encrypted traffic) 3. Collect application specific information (useful when targeting a single type of application) =3D out of network information 4. train the model 5. Online or offline testing 4. Apply application level policies. Data Format: Time : [Start, End, Unit, Number of Value, Sampling Period] Position: [Device ID, Port ID] Direction: IN / OUT Flow level metric: packet size distributions, number of packets, inter-arrival time distribution, (+ application specific knowledge : payload parsing) Message : Request: ask for the data Reply: Data Notice: For notification or others Policy: Control policy Best regards, jerome =20 Le 08/08/2017 =E0 06:52, Albert Cabellos a =E9crit : > Hi all > > Here=B4s another use-case: > > Use case N+2: QoE > Description: Collect low-level metrics (SNR, latency, jitter, > losses, etc) and measure QoE. Then use ML to understand what is the > relation between satisfactory QoE and the low-level metrics. As an > example learn that when delay>N then QoE is degraded, but when > M QoE cannot be measured directly over your network). This is useful to > understand how the network must be operated to provide satisfactory QoE= =2E > Process: 1. Low-level data collection and QoE measurement ; 2. > Training Model (input low-level metrics, output QoE); 3. Real-time > data capture and input; 4. Predict QoE; 5. Operate network to meet > target QoE requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, Jitter, > queue-size, etc > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Kind regards > > Albert > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen > wrote: > > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, > for applying AI into network management (NM), I=92d try to list som= e > Use Cases and propose a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose > your focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external > data which may influence the traffic. Predict the traffic in > short/long/specific term. Avoid the congestion or risk in previousl= y. > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Predication output; 5. Fix error and go back > to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might > be useful for some scenarios) > Service : [Service ID, Priority, > ...] (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic > flows. Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Output percentages; 5. Fix error and go back > to 3. > Data Format: Time : [Timestamp, Value type > (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts > and requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, > or other key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope > before Nov. Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --------------7DCE67CEE91FD93F0D9FA92C Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: 8bit Hi all,

Here is another use case about traffic classification.

Use case N+3: (encrypted) traffic classification

    Description: collect flow-level traffic metrics such as protocol information but also meta metrics such as distribution of packet sizes, inter-arrival times... Then use such information to label the trafic with the underlying application assuming that the granularity of classification may vary (type of application, exact application name, version...)
    Process: 1. collect packet information 2. flow reassembly (using directly flow format such as IPFIX might be possible but depends on the type of traffic, e.g. extracting the TLS application data is useful for encrypted traffic) 3. Collect application specific information (useful when targeting a single type of application) = out of network information 4. train the model 5. Online or offline testing 4. Apply application level policies.
    Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Flow level metric: packet size distributions, number of packets, inter-arrival time distribution,
                                 (+ application specific knowledge : payload parsing)

    Message :       Request: ask for the data
                           Reply: Data
                           Notice: For notification or others
                           Policy: Control policy


Best regards,
jerome
 
Le 08/08/2017 à 06:52, Albert Cabellos a écrit :
Hi all

Here´s another use-case:

Use case N+2: QoE
        Description: Collect low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then use ML to understand what is the relation between satisfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is satisfactory for the customers (please note that QoE cannot be measured directly over your network). This is useful to understand how the network must be operated to provide satisfactory QoE.
        Process: 1. Low-level data collection and QoE measurement ; 2. Training Model (input low-level metrics, output QoE); 3. Real-time data capture and input; 4. Predict QoE; 5. Operate network to meet target QoE requirement, go to 3.
        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Low-level metric : SNR, Delay, Jitter, queue-size, etc

        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Kind regards

Albert

On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote:
Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applying AI into network management (NM), I’d try to list some Use Cases and propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focused use cases or ideas.

Use case 1: Traffic Prediction
        Description: Collect the history traffic data and external data which may influence the traffic. Predict the traffic in short/long/specific term. Avoid the congestion or risk in previously.
        Process: 1. Data collection (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Predication output; 5. Fix error and go back to 3.
        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Route : [R1, R2, ..., RN]  (might be useful for some scenarios)
                                Service : [Service ID, Priority, ...]  (Not clear how to use it but seems useful)
                                Traffic: [T0, T1, T2, ..., TN]
        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Use case 2: QoS Management
        Description: Use multiple paths to distribute the traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.
        Process: 1. Data capture (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Output percentages; 5. Fix error and go back to 3.
        Data Format:    Time : [Timestamp, Value type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period]
                                Position: [Link ID, Device ID]
                                Value: [V0, V1, V2, ..., VN]
        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requirements
Sep. : Refining the cases and abstract the common elements
Oct. : Deeply analysis. Especially on Data Format, control flow, or other key points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. Hope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
        1.1 Network Management requirement
        1.2 TBD
2. Use Cases
        2.1 Traffic Prediction
        2.2 QoS Management
        3.3 TBD
3. Data Focus
        3.1 Data attribute
        3.2 Data format
        3.3 TBD
4. Aims
        4.1 Benchmarking Framework
        4.2 TBD
###ToC End###


Yansen

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IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet



_______________________________________________
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spamdiagnosticmetadata: NSPM Content-Type: multipart/alternative; boundary="_000_051F18D1621A4BF794F63C2D243F39C8telefonicacom_" MIME-Version: 1.0 X-OriginatorOrg: telefonica.com X-MS-Exchange-CrossTenant-originalarrivaltime: 08 Aug 2017 14:56:58.1542 (UTC) X-MS-Exchange-CrossTenant-fromentityheader: Hosted X-MS-Exchange-CrossTenant-id: 9744600e-3e04-492e-baa1-25ec245c6f10 X-MS-Exchange-Transport-CrossTenantHeadersStamped: DB6PR0601MB2167 Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 14:57:11 -0000 --_000_051F18D1621A4BF794F63C2D243F39C8telefonicacom_ Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 SGkgSmVyb21lLA0KDQpBZ3JlZWQuIFRoaXMgaXMgYSB1c2UgY2FzZSB3ZSBhcmUgdmVyeSBtdWNo 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No, score=-6.898 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-5, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id RgqYqJ1EZanC for ; Tue, 8 Aug 2017 07:59:30 -0700 (PDT) Received: from mail2-relais-roc.national.inria.fr (mail2-relais-roc.national.inria.fr [192.134.164.83]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5192C1324E0 for ; Tue, 8 Aug 2017 07:59:27 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,343,1498514400"; d="scan'208,217";a="286476029" Received: from unknown (HELO [192.168.1.108]) ([89.130.180.250]) by mail2-relais-roc.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 08 Aug 2017 16:59:23 +0200 To: "Diego R. Lopez" , Albert Cabellos , yanshen References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> Cc: "idnet@ietf.org" From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: <02682a50-626b-bd60-bf96-14748d1783e0@inria.fr> Date: Tue, 8 Aug 2017 16:59:19 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> Content-Type: multipart/alternative; boundary="------------864BE02D313F905C79C726E9" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 14:59:33 -0000 This is a multi-part message in MIME format. --------------864BE02D313F905C79C726E9 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 8bit 100% agree with you. I was far from being exhaustive as traffic features may depend on types of traffic (kin of sub use cases) jerome Le 08/08/2017 à 16:56, Diego R. Lopez a écrit : > > Hi Jerome, > > > > Agreed. This is a use case we are very much interested in, and > actually working in it now. Just let me say we are trying to evaluate > which are the significant features of the flow to perform a proper > classification, depending on the flow nature (TLS, DTLS, QUIC, > IPsec…), and that would define the concrete data to be exchanged or > stored. > > > > Be goode, > > > > -- > > "Esta vez no fallaremos, Doctor Infierno" > > > > Dr Diego R. Lopez > > Telefonica I+D > > http://people.tid.es/diego.lopez/ > > > > e-mail: diego.r.lopez@telefonica.com > > Tel: +34 913 129 041 > > Mobile: +34 682 051 091 > > ----------------------------------- > > > > On 8/8/2017, 16:49 , "IDNET on behalf of Jérôme François" > on behalf of > jerome.francois@inria.fr > wrote: > > > > Hi all, > > Here is another use case about traffic classification. > > Use case N+3: (encrypted) traffic classification > > Description: collect flow-level traffic metrics such as protocol > information but also meta metrics such as distribution of packet > sizes, inter-arrival times... Then use such information to label the > trafic with the underlying application assuming that the granularity > of classification may vary (type of application, exact application > name, version...) > Process: 1. collect packet information 2. flow reassembly (using > directly flow format such as IPFIX might be possible but depends on > the type of traffic, e.g. extracting the TLS application data is > useful for encrypted traffic) 3. Collect application specific > information (useful when targeting a single type of application) = out > of network information 4. train the model 5. Online or offline testing > 4. Apply application level policies. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Flow level metric: packet size > distributions, number of packets, inter-arrival time distribution, > (+ application specific knowledge : > payload parsing) > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > Best regards, > jerome > > > Le 08/08/2017 à 06:52, Albert Cabellos a écrit : > > Hi all > > > > Here´s another use-case: > > > > Use case N+2: QoE > Description: Collect low-level metrics (SNR, latency, > jitter, losses, etc) and measure QoE. Then use ML to understand > what is the relation between satisfactory QoE and the low-level > metrics. As an example learn that when delay>N then QoE is > degraded, but when M customers (please note that QoE cannot be measured directly over > your network). This is useful to understand how the network must > be operated to provide satisfactory QoE. > Process: 1. Low-level data collection and QoE measurement > ; 2. Training Model (input low-level metrics, output QoE); 3. > Real-time data capture and input; 4. Predict QoE; 5. Operate > network to meet target QoE requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, > Jitter, queue-size, etc > > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > > Kind regards > > > > Albert > > > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen > wrote: > > Dear all, > > Since we plan to organize a dedicated session in NMRG, > IETF100, for applying AI into network management (NM), I’d try > to list some Use Cases and propose a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and > propose your focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and > external data which may influence the traffic. Predict the > traffic in short/long/specific term. Avoid the congestion or > risk in previously. > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Predication output; 5. Fix error and go > back to 3. > Data Format: Time : [Start, End, Unit, Number of > Value, Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] > (might be useful for some scenarios) > Service : [Service ID, > Priority, ...] (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the > traffic flows. Adjust the percentages. Avoid congestion and > ensure QoS. > Process: 1. Data capture (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Output percentages; 5. Fix error and go > back to 3. > Data Format: Time : [Timestamp, Value type > (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough > thoughts and requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control > flow, or other key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope > before Nov. Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > > ------------------------------------------------------------------------ > > Este mensaje y sus adjuntos se dirigen exclusivamente a su > destinatario, puede contener información privilegiada o confidencial y > es para uso exclusivo de la persona o entidad de destino. Si no es > usted. el destinatario indicado, queda notificado de que la lectura, > utilización, divulgación y/o copia sin autorización puede estar > prohibida en virtud de la legislación vigente. Si ha recibido este > mensaje por error, le rogamos que nos lo comunique inmediatamente por > esta misma vía y proceda a su destrucción. > > The information contained in this transmission is privileged and > confidential information intended only for the use of the individual > or entity named above. If the reader of this message is not the > intended recipient, you are hereby notified that any dissemination, > distribution or copying of this communication is strictly prohibited. > If you have received this transmission in error, do not read it. > Please immediately reply to the sender that you have received this > communication in error and then delete it. > > Esta mensagem e seus anexos se dirigem exclusivamente ao seu > destinatário, pode conter informação privilegiada ou confidencial e é > para uso exclusivo da pessoa ou entidade de destino. Se não é vossa > senhoria o destinatário indicado, fica notificado de que a leitura, > utilização, divulgação e/ou cópia sem autorização pode estar proibida > em virtude da legislação vigente. Se recebeu esta mensagem por erro, > rogamos-lhe que nos o comunique imediatamente por esta mesma via e > proceda a sua destruição --------------864BE02D313F905C79C726E9 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: 8bit 100% agree with you. I was far from being exhaustive as traffic features may depend on types of traffic (kin of sub use cases)

jerome
Le 08/08/2017 à 16:56, Diego R. Lopez a écrit :

Hi Jerome,

 

Agreed. This is a use case we are very much interested in, and actually working in it now. Just let me say we are trying to evaluate which are the significant features of the flow to perform a proper classification, depending on the flow nature (TLS, DTLS, QUIC, IPsec…), and that would define the concrete data to be exchanged or stored.

 

Be goode,

 

--

"Esta vez no fallaremos, Doctor Infierno"

 

Dr Diego R. Lopez

Telefonica I+D

http://people.tid.es/diego.lopez/

 

e-mail: diego.r.lopez@telefonica.com

Tel:        +34 913 129 041

Mobile: +34 682 051 091

-----------------------------------

 

On 8/8/2017, 16:49 , "IDNET on behalf of Jérôme François" <idnet-bounces@ietf.org on behalf of jerome.francois@inria.fr> wrote:

 

Hi all,

Here is another use case about traffic classification.

Use case N+3: (encrypted) traffic classification

    Description: collect flow-level traffic metrics such as protocol information but also meta metrics such as distribution of packet sizes, inter-arrival times... Then use such information to label the trafic with the underlying application assuming that the granularity of classification may vary (type of application, exact application name, version...)
    Process: 1. collect packet information 2. flow reassembly (using directly flow format such as IPFIX might be possible but depends on the type of traffic, e.g. extracting the TLS application data is useful for encrypted traffic) 3. Collect application specific information (useful when targeting a single type of application) = out of network information 4. train the model 5. Online or offline testing 4. Apply application level policies.
    Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Flow level metric: packet size distributions, number of packets, inter-arrival time distribution,
                                 (+ application specific knowledge : payload parsing)

    Message :       Request: ask for the data
                           Reply: Data
                           Notice: For notification or others
                           Policy: Control policy


Best regards,
jerome
 

Le 08/08/2017 à 06:52, Albert Cabellos a écrit :

Hi all

 

Here´s another use-case:

 

Use case N+2: QoE
        Description: Collect low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then use ML to understand what is the relation between satisfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is satisfactory for the customers (please note that QoE cannot be measured directly over your network). This is useful to understand how the network must be operated to provide satisfactory QoE.
        Process: 1. Low-level data collection and QoE measurement ; 2. Training Model (input low-level metrics, output QoE); 3. Real-time data capture and input; 4. Predict QoE; 5. Operate network to meet target QoE requirement, go to 3.
        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Low-level metric : SNR, Delay, Jitter, queue-size, etc


        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

 

Kind regards

 

Albert

 

On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote:

Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applying AI into network management (NM), I’d try to list some Use Cases and propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focused use cases or ideas.

Use case 1: Traffic Prediction
        Description: Collect the history traffic data and external data which may influence the traffic. Predict the traffic in short/long/specific term. Avoid the congestion or risk in previously.
        Process: 1. Data collection (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Predication output; 5. Fix error and go back to 3.
        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]
                                Position: [Device ID, Port ID]
                                Direction: IN / OUT
                                Route : [R1, R2, ..., RN]  (might be useful for some scenarios)
                                Service : [Service ID, Priority, ...]  (Not clear how to use it but seems useful)
                                Traffic: [T0, T1, T2, ..., TN]
        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Use case 2: QoS Management
        Description: Use multiple paths to distribute the traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.
        Process: 1. Data capture (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Output percentages; 5. Fix error and go back to 3.
        Data Format:    Time : [Timestamp, Value type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period]
                                Position: [Link ID, Device ID]
                                Value: [V0, V1, V2, ..., VN]
        Message :       Request: ask for the data
                                Reply: Data
                                Notice: For notification or others
                                Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requirements
Sep. : Refining the cases and abstract the common elements
Oct. : Deeply analysis. Especially on Data Format, control flow, or other key points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. Hope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
        1.1 Network Management requirement
        1.2 TBD
2. Use Cases
        2.1 Traffic Prediction
        2.2 QoS Management
        3.3 TBD
3. Data Focus
        3.1 Data attribute
        3.2 Data format
        3.3 TBD
4. Aims
        4.1 Benchmarking Framework
        4.2 TBD
###ToC End###


Yansen

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(mail-pg0-x22f.google.com [IPv6:2607:f8b0:400e:c05::22f]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 8D38E132516 for ; Tue, 8 Aug 2017 08:21:18 -0700 (PDT) Received: by mail-pg0-x22f.google.com with SMTP id y129so16078791pgy.4 for ; Tue, 08 Aug 2017 08:21:18 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:sender:in-reply-to:references:from:date:message-id :subject:to:cc:content-transfer-encoding; bh=tXjpNYGkCwUha6AAL3UUAZ7sah/MUBscBgTJaF685io=; b=UxDuKJbnf/UhvW7epyxmVHxD99sTlLbFHqF4/lX5HWePnYko0FTd353q8s/bDGokjL rA0miVJxH5WOv96RfUrd3ca6AA88VyVRgN6v+cpIDrj/UskYTbkLVOA/r0AQElhXoiQu AU90YLCDw/3mzeflvTjgLySGuAWAmBbv+xFyUDvXH7cwMln5GX1AK/ws1tbC8uNz8RF/ +8mWJZdgJ6GjN9aB5qh8QpNWXcR04cEhZlJtQ/BlxFuND/txd67EtmS3PBk65kvk1lud 8YgGiLxK2cbVS/bl1I9Kwy8+V8j9E+ZgcQA6ozBCXilNm8q7fKR3VvZxrRYRPdbD0NCZ iB8w== X-Google-DKIM-Signature: v=1; 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References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> <02682a50-626b-bd60-bf96-14748d1783e0@inria.fr> From: Stenio Fernandes Date: Tue, 8 Aug 2017 11:20:37 -0400 X-Google-Sender-Auth: vMgt_Zjqv6Sd_6bUeJMRkN8sjiM Message-ID: To: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Cc: "Diego R. Lopez" , Albert Cabellos , yanshen , "idnet@ietf.org" Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Archived-At: X-Mailman-Approved-At: Tue, 08 Aug 2017 16:27:58 -0700 Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 08 Aug 2017 15:22:11 -0000 Hi Jerome, Diego, et al, Those are excellent use cases. I have some published work on applied machine learning to computer networking problems, including flow-based traffic classification. I think another use case would be applying unsupervised learning techniques for anomaly detection. I can elaborate further on this. Stenio On Tue, Aug 8, 2017 at 10:59 AM, J=C3=A9r=C3=B4me Fran=C3=A7ois wrote: > 100% agree with you. I was far from being exhaustive as traffic features = may > depend on types of traffic (kin of sub use cases) > > jerome > > Le 08/08/2017 =C3=A0 16:56, Diego R. Lopez a =C3=A9crit : > > Hi Jerome, > > > > Agreed. This is a use case we are very much interested in, and actually > working in it now. Just let me say we are trying to evaluate which are th= e > significant features of the flow to perform a proper classification, > depending on the flow nature (TLS, DTLS, QUIC, IPsec=E2=80=A6), and that = would > define the concrete data to be exchanged or stored. > > > > Be goode, > > > > -- > > "Esta vez no fallaremos, Doctor Infierno" > > > > Dr Diego R. Lopez > > Telefonica I+D > > http://people.tid.es/diego.lopez/ > > > > e-mail: diego.r.lopez@telefonica.com > > Tel: +34 913 129 041 > > Mobile: +34 682 051 091 > > ----------------------------------- > > > > On 8/8/2017, 16:49 , "IDNET on behalf of J=C3=A9r=C3=B4me Fran=C3=A7ois" > wrote: > > > > Hi all, > > Here is another use case about traffic classification. > > Use case N+3: (encrypted) traffic classification > > Description: collect flow-level traffic metrics such as protocol > information but also meta metrics such as distribution of packet sizes, > inter-arrival times... Then use such information to label the trafic with > the underlying application assuming that the granularity of classificatio= n > may vary (type of application, exact application name, version...) > Process: 1. collect packet information 2. flow reassembly (using > directly flow format such as IPFIX might be possible but depends on the t= ype > of traffic, e.g. extracting the TLS application data is useful for encryp= ted > traffic) 3. Collect application specific information (useful when targeti= ng > a single type of application) =3D out of network information 4. train the > model 5. Online or offline testing 4. Apply application level policies. > Data Format: Time : [Start, End, Unit, Number of Value, Sampling > Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Flow level metric: packet size > distributions, number of packets, inter-arrival time distribution, > (+ application specific knowledge : payl= oad > parsing) > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > Best regards, > jerome > > > Le 08/08/2017 =C3=A0 06:52, Albert Cabellos a =C3=A9crit : > > Hi all > > > > Here=C2=B4s another use-case: > > > > Use case N+2: QoE > Description: Collect low-level metrics (SNR, latency, jitter, > losses, etc) and measure QoE. Then use ML to understand what is the relat= ion > between satisfactory QoE and the low-level metrics. As an example learn t= hat > when delay>N then QoE is degraded, but when M satisfactory for the customers (please note that QoE cannot be measured > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > Process: 1. Low-level data collection and QoE measurement ; 2. > Training Model (input low-level metrics, output QoE); 3. Real-time data > capture and input; 4. Predict QoE; 5. Operate network to meet target QoE > requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, Sampli= ng > Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, Jitter, > queue-size, etc > > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > > Kind regards > > > > Albert > > > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen wrote: > > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, for apply= ing > AI into network management (NM), I=E2=80=99d try to list some Use Cases a= nd propose > a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose your > focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external data > which may influence the traffic. Predict the traffic in short/long/specif= ic > term. Avoid the congestion or risk in previously. > Process: 1. Data collection (e.g. traffic sample of physical/logi= cal > port ); 2. Training Model; 3. Real-time data capture and input; 4. > Predication output; 5. Fix error and go back to 3. > Data Format: Time : [Start, End, Unit, Number of Value, Sampli= ng > Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might be usef= ul > for some scenarios) > Service : [Service ID, Priority, ...] (N= ot > clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic flows. > Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of physical/logical > port ); 2. Training Model; 3. Real-time data capture and input; 4. Output > percentages; 5. Fix error and go back to 3. > Data Format: Time : [Timestamp, Value type (Delay/Packet > Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope before Nov. > Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > > ________________________________ > > Este mensaje y sus adjuntos se dirigen exclusivamente a su destinatario, > puede contener informaci=C3=B3n privilegiada o confidencial y es para uso > exclusivo de la persona o entidad de destino. Si no es usted. el > destinatario indicado, queda notificado de que la lectura, utilizaci=C3= =B3n, > divulgaci=C3=B3n y/o copia sin autorizaci=C3=B3n puede estar prohibida en= virtud de la > legislaci=C3=B3n vigente. Si ha recibido este mensaje por error, le rogam= os que > nos lo comunique inmediatamente por esta misma v=C3=ADa y proceda a su > destrucci=C3=B3n. > > The information contained in this transmission is privileged and > confidential information intended only for the use of the individual or > entity named above. If the reader of this message is not the intended > recipient, you are hereby notified that any dissemination, distribution o= r > copying of this communication is strictly prohibited. If you have receive= d > this transmission in error, do not read it. Please immediately reply to t= he > sender that you have received this communication in error and then delete > it. > > Esta mensagem e seus anexos se dirigem exclusivamente ao seu destinat=C3= =A1rio, > pode conter informa=C3=A7=C3=A3o privilegiada ou confidencial e =C3=A9 pa= ra uso exclusivo > da pessoa ou entidade de destino. Se n=C3=A3o =C3=A9 vossa senhoria o des= tinat=C3=A1rio > indicado, fica notificado de que a leitura, utiliza=C3=A7=C3=A3o, divulga= =C3=A7=C3=A3o e/ou > c=C3=B3pia sem autoriza=C3=A7=C3=A3o pode estar proibida em virtude da le= gisla=C3=A7=C3=A3o vigente. > Se recebeu esta mensagem por erro, rogamos-lhe que nos o comunique > imediatamente por esta mesma via e proceda a sua destrui=C3=A7=C3=A3o > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --=20 Prof. Stenio Fernandes CIn/UFPE http://www.steniofernandes.com From nobody Tue Aug 8 17:00:52 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DEC6B1200C5 for ; Tue, 8 Aug 2017 17:00:50 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.2 X-Spam-Level: X-Spam-Status: No, score=-4.2 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 4oEzEw8HsmOn for ; Tue, 8 Aug 2017 17:00:48 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id 9DD3E131CB2 for ; Tue, 8 Aug 2017 17:00:48 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v7900gvK005777; Wed, 9 Aug 2017 09:00:42 +0900 (JST) Received: from mail2.nict.go.jp (mail2.nict.go.jp [133.243.18.15]) by gw2.nict.go.jp with ESMTP id v7900gbx005710; Wed, 9 Aug 2017 09:00:42 +0900 (JST) Received: from spectre (unknown [133.243.115.68]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail2.nict.go.jp (NICT Mail Spool Server2) with ESMTPS id 35214CE25; Wed, 9 Aug 2017 09:00:42 +0900 (JST) Date: Wed, 9 Aug 2017 09:00:40 +0900 From: Pedro Martinez-Julia To: yanshen Cc: "idnet@ietf.org" Message-ID: <20170809000040.GM14964@spectre> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> User-Agent: Mutt/1.8.3 (2017-05-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 09 Aug 2017 00:00:51 -0000 Dear colleagues, I want to declare my interest and add my rough/initial contribution about the benchmarking framework. A proper benchmarking framework comprises a set of reference procedures, methods, and models that can (or better *must*) be followed to assess the quality of an AI mechanism proposed to be applied to the network management/control area. It is therefore essential to standardize such framework but it is even more important for it to have demonstrated widely accepted effectiveness (not just assumed), so it must be derived or adapted from a framework with demonstrated usefulness that already exists in other area with similar objectives and overall methodology. Moreover, and much more specific to the IDNET topics, is the inclusion, dependency, or just the general relation of a standard format enforced to the data that is used (input) and produced (output) by the framework, so a kind of "data market" can arise without requiring to transform the data. The initial scope of input/output data would be the datasets, but also the new knowledge items that are stated as a result of applying the benchmarking procedures defined by the framework, which can be collected together to build a database of benchmark results, or just contrasted with other existing entries in the database to know the position of the solution just evaluated. This increases the usefulness of IDNET. That is all for now. Let's be in contact and go further in our effort to get a proper document (and session) in the IETF 100. If it is achieved, I want to have some time slot to present this idea (~ 10 min), so please count on me to organize the schedule of the potential session. Thank you very much. Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Tue Aug 8 19:30:31 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 5F785131D24 for ; Tue, 8 Aug 2017 19:30:26 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.811 X-Spam-Level: X-Spam-Status: No, score=-1.811 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, RCVD_IN_DNSWL_NONE=-0.0001, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, T_DKIM_INVALID=0.01] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=neutral reason="invalid (public key: not available)" header.d=ferlin.io Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 5uJN_Gyrs1hF for ; Tue, 8 Aug 2017 19:30:23 -0700 (PDT) Received: from n1nlsmtp02.shr.prod.ams1.secureserver.net (n1nlsmtp02.prod.ams1.secureserver.net [188.121.43.194]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id A8063131D33 for ; Tue, 8 Aug 2017 19:30:05 -0700 (PDT) Received: from n1plcpnl0047.prod.ams1.secureserver.net ([46.252.205.173]) by : HOSTING RELAY : with SMTP id fGk1dtQUVmE6LfGk1dtEPH; Tue, 08 Aug 2017 19:29:02 -0700 DKIM-Signature: v=1; a=rsa-sha256; q=dns/txt; c=relaxed/relaxed; d=ferlin.io; s=default; h=Content-Transfer-Encoding:Content-Type:Cc:To:Subject:Message-ID :Date:From:References:In-Reply-To:MIME-Version:Sender:Reply-To:Content-ID: Content-Description:Resent-Date:Resent-From:Resent-Sender:Resent-To:Resent-Cc :Resent-Message-ID:List-Id:List-Help:List-Unsubscribe:List-Subscribe: List-Post:List-Owner:List-Archive; bh=ZtNn0lrwIIYrcvV8tM63iYcSTn+MjwCXHrKorJmALPA=; b=XnYbzrSbp/P9EIwje39O4xMjqX OQHkxjmqlmFtw+x/yD6L8mf0ISaXpHF427kFN8XFtq3SRMXubUtpX2OGOFVAyo03+JiAGbYnNGL/z t8slkERKPE1CnKumbNbaTV15tRdmkOUot/CdcNvMFm+G0n4fAEp9qEHiLSVEgGRg6QTa2G9EiQQh/ 9UXR03rhAdjeJJCKT6OKBGJnyhXF5sj2rsOOHIMY6/Xbp25dt4ujEV/thp3B/CvI2qzdOPjv0MGO8 rloMqF1wc1cSwKJYAhayBTKLFJyQ6lPHzE2fXaSHbo0esoLPyqLvYnZ0fedvTBgTHaY2U9CBeRLcP zNOdty4Q==; Received: from mail-pg0-f48.google.com ([74.125.83.48]:37821) by n1plcpnl0047.prod.ams1.secureserver.net with esmtpsa (TLSv1.2:ECDHE-RSA-AES128-GCM-SHA256:128) (Exim 4.88) (envelope-from ) id 1dfGk1-001tr2-N1 for idnet@ietf.org; Tue, 08 Aug 2017 19:29:01 -0700 Received: by mail-pg0-f48.google.com with SMTP id y129so22127446pgy.4 for ; Tue, 08 Aug 2017 19:29:01 -0700 (PDT) X-Gm-Message-State: AHYfb5gakNBoMExPPEK1KGPTXs3aCX2JDwSpvJgIoBkQ5+O7PTvvV66r 9gl3JQDxYxoExByq9CmiG/azBDX7Pw== X-Received: by 10.84.218.2 with SMTP id q2mr7351813pli.320.1502245740664; Tue, 08 Aug 2017 19:29:00 -0700 (PDT) MIME-Version: 1.0 Received: by 10.100.182.167 with HTTP; Tue, 8 Aug 2017 19:28:40 -0700 (PDT) In-Reply-To: References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> <02682a50-626b-bd60-bf96-14748d1783e0@inria.fr> From: Simone Ferlin Date: Wed, 9 Aug 2017 11:28:40 +0900 X-Gmail-Original-Message-ID: Message-ID: To: Stenio Fernandes Cc: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , yanshen , "idnet@ietf.org" , Albert Cabellos , "Diego R. Lopez" Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - n1plcpnl0047.prod.ams1.secureserver.net X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - ferlin.io X-Get-Message-Sender-Via: n1plcpnl0047.prod.ams1.secureserver.net: authenticated_id: simone@ferlin.io X-Authenticated-Sender: n1plcpnl0047.prod.ams1.secureserver.net: simone@ferlin.io X-Source: X-Source-Args: X-Source-Dir: X-CMAE-Envelope: MS4wfIfTpdWajDZsA5OJwzsEA6O8u8nE97IA1pChcP6tWbbJIL3QMbvM1W9LdE0GO/z1nnQXQshumE83Ky4m1PmxDzcJ+SUB+0Xjon69ZWJ3TFGpDbYkwskq +AzzzncyT7IxJ0rVgNB/4gm3Wm4E1ZQQd5ET5hC3vtfandlqeneezhOqAsKt84KUQjofBcqCL5VsMNC3FX4uSA+6S5+LD3FtcqE= Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 09 Aug 2017 02:30:26 -0000 Dear Jerome, Very interesting use-case, +1 support. I have interest in such activities for traffic classification, anomaly detection in particular for encrypted traffic. > On Wed, Aug 9, 2017 at 12:20 AM, Stenio Fernandes wrot= e: >> Hi Jerome, Diego, et al, >> >> Those are excellent use cases. I have some published work on applied >> machine learning to computer networking problems, including flow-based >> traffic classification. I think another use case would be applying >> unsupervised learning techniques for anomaly detection. I can >> elaborate further on this. >> >> Stenio >> >> On Tue, Aug 8, 2017 at 10:59 AM, J=C3=A9r=C3=B4me Fran=C3=A7ois >> wrote: >>> 100% agree with you. I was far from being exhaustive as traffic feature= s may >>> depend on types of traffic (kin of sub use cases) >>> >>> jerome >>> >>> Le 08/08/2017 =C3=A0 16:56, Diego R. Lopez a =C3=A9crit : >>> >>> Hi Jerome, >>> >>> >>> >>> Agreed. This is a use case we are very much interested in, and actually >>> working in it now. Just let me say we are trying to evaluate which are = the >>> significant features of the flow to perform a proper classification, >>> depending on the flow nature (TLS, DTLS, QUIC, IPsec=E2=80=A6), and tha= t would >>> define the concrete data to be exchanged or stored. >>> >>> >>> >>> Be goode, >>> >>> >>> >>> -- >>> >>> "Esta vez no fallaremos, Doctor Infierno" >>> >>> >>> >>> Dr Diego R. Lopez >>> >>> Telefonica I+D >>> >>> http://people.tid.es/diego.lopez/ >>> >>> >>> >>> e-mail: diego.r.lopez@telefonica.com >>> >>> Tel: +34 913 129 041 >>> >>> Mobile: +34 682 051 091 >>> >>> ----------------------------------- >>> >>> >>> >>> On 8/8/2017, 16:49 , "IDNET on behalf of J=C3=A9r=C3=B4me Fran=C3=A7ois= " >>> wrote: >>> >>> >>> >>> Hi all, >>> >>> Here is another use case about traffic classification. >>> >>> Use case N+3: (encrypted) traffic classification >>> >>> Description: collect flow-level traffic metrics such as protocol >>> information but also meta metrics such as distribution of packet sizes, >>> inter-arrival times... Then use such information to label the trafic wi= th >>> the underlying application assuming that the granularity of classificat= ion >>> may vary (type of application, exact application name, version...) >>> Process: 1. collect packet information 2. flow reassembly (using >>> directly flow format such as IPFIX might be possible but depends on the= type >>> of traffic, e.g. extracting the TLS application data is useful for encr= ypted >>> traffic) 3. Collect application specific information (useful when targe= ting >>> a single type of application) =3D out of network information 4. train t= he >>> model 5. Online or offline testing 4. Apply application level policies. >>> Data Format: Time : [Start, End, Unit, Number of Value, Sampling >>> Period] >>> Position: [Device ID, Port ID] >>> Direction: IN / OUT >>> Flow level metric: packet size >>> distributions, number of packets, inter-arrival time distribution, >>> (+ application specific knowledge : pa= yload >>> parsing) >>> >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> >>> Best regards, >>> jerome >>> >>> >>> Le 08/08/2017 =C3=A0 06:52, Albert Cabellos a =C3=A9crit : >>> >>> Hi all >>> >>> >>> >>> Here=C2=B4s another use-case: >>> >>> >>> >>> Use case N+2: QoE >>> Description: Collect low-level metrics (SNR, latency, jitter, >>> losses, etc) and measure QoE. Then use ML to understand what is the rel= ation >>> between satisfactory QoE and the low-level metrics. As an example learn= that >>> when delay>N then QoE is degraded, but when M>> satisfactory for the customers (please note that QoE cannot be measured >>> directly over your network). This is useful to understand how the netwo= rk >>> must be operated to provide satisfactory QoE. >>> Process: 1. Low-level data collection and QoE measurement ; 2. >>> Training Model (input low-level metrics, output QoE); 3. Real-time data >>> capture and input; 4. Predict QoE; 5. Operate network to meet target Qo= E >>> requirement, go to 3. >>> Data Format: Time : [Start, End, Unit, Number of Value, Samp= ling >>> Period] >>> Position: [Device ID, Port ID] >>> Direction: IN / OUT >>> Low-level metric : SNR, Delay, Jitter, >>> queue-size, etc >>> >>> >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> >>> >>> Kind regards >>> >>> >>> >>> Albert >>> >>> >>> >>> On Wed, Aug 2, 2017 at 7:12 PM, yanshen wrote: >>> >>> Dear all, >>> >>> Since we plan to organize a dedicated session in NMRG, IETF100, for app= lying >>> AI into network management (NM), I=E2=80=99d try to list some Use Cases= and propose >>> a roadmap and ToC before Nov. >>> >>> These might be rough. You are welcome to refine them and propose your >>> focused use cases or ideas. >>> >>> Use case 1: Traffic Prediction >>> Description: Collect the history traffic data and external data >>> which may influence the traffic. Predict the traffic in short/long/spec= ific >>> term. Avoid the congestion or risk in previously. >>> Process: 1. Data collection (e.g. traffic sample of physical/lo= gical >>> port ); 2. Training Model; 3. Real-time data capture and input; 4. >>> Predication output; 5. Fix error and go back to 3. >>> Data Format: Time : [Start, End, Unit, Number of Value, Samp= ling >>> Period] >>> Position: [Device ID, Port ID] >>> Direction: IN / OUT >>> Route : [R1, R2, ..., RN] (might be us= eful >>> for some scenarios) >>> Service : [Service ID, Priority, ...] = (Not >>> clear how to use it but seems useful) >>> Traffic: [T0, T1, T2, ..., TN] >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> Use case 2: QoS Management >>> Description: Use multiple paths to distribute the traffic flows= . >>> Adjust the percentages. Avoid congestion and ensure QoS. >>> Process: 1. Data capture (e.g. traffic sample of physical/logic= al >>> port ); 2. Training Model; 3. Real-time data capture and input; 4. Outp= ut >>> percentages; 5. Fix error and go back to 3. >>> Data Format: Time : [Timestamp, Value type (Delay/Packet >>> Loss/...), Unit, Number of Value, Sampling Period] >>> Position: [Link ID, Device ID] >>> Value: [V0, V1, V2, ..., VN] >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> Use case N: Waiting for your Ideas >>> >>> Also I suggest a roadmap before Nov if possible. >>> >>> ### Roadmap ### >>> Aug. : Collecting the use cases (related with NM). Rough thoughts and >>> requirements >>> Sep. : Refining the cases and abstract the common elements >>> Oct. : Deeply analysis. Especially on Data Format, control flow, or oth= er >>> key points >>> Nov.: F2F discussions on IETF100 >>> ### Roadmap End ### >>> >>> A rough ToC is listed in following. We may take it as a scope before No= v. >>> Hope that the content could become the draft of draft. >>> >>> ###Table of Content### >>> 1. Gap and Requirement Analysis >>> 1.1 Network Management requirement >>> 1.2 TBD >>> 2. Use Cases >>> 2.1 Traffic Prediction >>> 2.2 QoS Management >>> 3.3 TBD >>> 3. Data Focus >>> 3.1 Data attribute >>> 3.2 Data format >>> 3.3 TBD >>> 4. Aims >>> 4.1 Benchmarking Framework >>> 4.2 TBD >>> ###ToC End### >>> >>> >>> Yansen >>> >>> _______________________________________________ >>> IDNET mailing list >>> IDNET@ietf.org >>> https://www.ietf.org/mailman/listinfo/idnet >>> >>> >>> >>> >>> >>> >>> _______________________________________________ >>> >>> IDNET mailing list >>> >>> IDNET@ietf.org >>> >>> https://www.ietf.org/mailman/listinfo/idnet >>> >>> >>> >>> >>> ________________________________ >>> >>> Este mensaje y sus adjuntos se dirigen exclusivamente a su destinatario= , >>> puede contener informaci=C3=B3n privilegiada o confidencial y es para u= so >>> exclusivo de la persona o entidad de destino. Si no es usted. el >>> destinatario indicado, queda notificado de que la lectura, utilizaci=C3= =B3n, >>> divulgaci=C3=B3n y/o copia sin autorizaci=C3=B3n puede estar prohibida = en virtud de la >>> legislaci=C3=B3n vigente. Si ha recibido este mensaje por error, le rog= amos que >>> nos lo comunique inmediatamente por esta misma v=C3=ADa y proceda a su >>> destrucci=C3=B3n. >>> >>> The information contained in this transmission is privileged and >>> confidential information intended only for the use of the individual or >>> entity named above. If the reader of this message is not the intended >>> recipient, you are hereby notified that any dissemination, distribution= or >>> copying of this communication is strictly prohibited. If you have recei= ved >>> this transmission in error, do not read it. Please immediately reply to= the >>> sender that you have received this communication in error and then dele= te >>> it. >>> >>> Esta mensagem e seus anexos se dirigem exclusivamente ao seu destinat= =C3=A1rio, >>> pode conter informa=C3=A7=C3=A3o privilegiada ou confidencial e =C3=A9 = para uso exclusivo >>> da pessoa ou entidade de destino. Se n=C3=A3o =C3=A9 vossa senhoria o d= estinat=C3=A1rio >>> indicado, fica notificado de que a leitura, utiliza=C3=A7=C3=A3o, divul= ga=C3=A7=C3=A3o e/ou >>> c=C3=B3pia sem autoriza=C3=A7=C3=A3o pode estar proibida em virtude da = legisla=C3=A7=C3=A3o vigente. >>> Se recebeu esta mensagem por erro, rogamos-lhe que nos o comunique >>> imediatamente por esta mesma via e proceda a sua destrui=C3=A7=C3=A3o >>> >>> >>> >>> _______________________________________________ >>> IDNET mailing list >>> IDNET@ietf.org >>> https://www.ietf.org/mailman/listinfo/idnet >>> >> >> >> >> -- >> Prof. Stenio Fernandes >> CIn/UFPE >> http://www.steniofernandes.com >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet From nobody Tue Aug 8 19:44:27 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DEF0012426E for ; Tue, 8 Aug 2017 19:44:25 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.999 X-Spam-Level: X-Spam-Status: No, score=-1.999 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id LaR3RKrqecKp for ; Tue, 8 Aug 2017 19:44:23 -0700 (PDT) Received: from mail-qt0-x22f.google.com (mail-qt0-x22f.google.com [IPv6:2607:f8b0:400d:c0d::22f]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 2395D124B09 for ; Tue, 8 Aug 2017 19:44:23 -0700 (PDT) Received: by mail-qt0-x22f.google.com with SMTP id 16so29571934qtz.4 for ; Tue, 08 Aug 2017 19:44:23 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=0zgO9TddZUT//1fvg/MalxCZIHxKP5rfg/lP16P5UK4=; b=mkMMTOWXf0+IsuEoBwvjfSCM+9ALzLZQqQjkOy8SsA4CTVY4X8gw+EPdhAjoLxSGOO 11B/3eXDW7S2+iIEIfSNiJ2/DDR8dsBJNe4+fYLwBnIESBzuP6zl4EcSj/K54ZKvaBtn 8/T8Vrt/t7MsIt0m1l5ZxeZPPvC4YlSBYCvOCuzhuonLmqrpQ8GI1/IzMX7NFn5ROieY dsu1rs7PM9Kybm7vJFom5kDAL+xEB2kygaPuidIcatnRRR1IqhuHKyq0ZlZLk/GtX3PQ 1FlvVK+EH498cfDPZ6MMy8DJjF6/lQYJjaWgT0I0sLZXTW/e/Lc9q5Cs0n28thqWnwmT OzDQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=0zgO9TddZUT//1fvg/MalxCZIHxKP5rfg/lP16P5UK4=; b=k6HZJkGAHlIVxoN9wVsd9mD0kuRrZZSyy3gXmeLvzON9kpqbq7mMY3okQxkbnxCLws DXhNbovFW9JFGRsJb685xSP0fWmIvTWFpWr4iGxkVpFwfIOQ2V2bfp2YnKhz48s9HiEx CAQN6fMAH+ncihgu9i7GlskTLncEfC458wJGG3oUX5aNEs776+309fhzWn5tQibjQCEk NuSkvo0/24H9I+CsnQ0F0NXdSRclNVh6n6sf+kP9XUwon4eP0TM3j3WbpA5bWiOAjX5T pqofDKK2RwLPxhWKCW8DB0h048ZFS+GeOw/Hih2IyPe4ksip71sGLhoJs1Yjn65/Uvmy 5FJA== X-Gm-Message-State: AHYfb5je2GlK6m139UT1b6gEhNQxnoRaUvoTa7N2zxr/rewllSCSxySV 3jqZxWfaYtEGMsPvquqxlERJVqcoTOBH X-Received: by 10.200.33.164 with SMTP id 33mr9384077qty.109.1502246662031; Tue, 08 Aug 2017 19:44:22 -0700 (PDT) MIME-Version: 1.0 Received: by 10.140.86.52 with HTTP; Tue, 8 Aug 2017 19:44:21 -0700 (PDT) In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D297B223@DGGEMM505-MBS.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D297B223@DGGEMM505-MBS.china.huawei.com> From: Albert Cabellos Date: Wed, 9 Aug 2017 11:44:21 +0900 Message-ID: To: yanshen Cc: "idnet@ietf.org" , =?UTF-8?B?w5Z6Z8O8IEFsYXk=?= Content-Type: multipart/alternative; boundary="001a113f382ca7cdc30556490e2a" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 09 Aug 2017 02:44:26 -0000 --001a113f382ca7cdc30556490e2a Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hi Yanshen In my view they are not the same use-case - QoS use-case: As far as I understand here you aim to configure the routing/switching infrastructure to achieve QoS for flows. - QoE use-case: Here the idea is that QoE can not typically be measured directly over the network, it has to be measured either over the application (e.g., buffering in on-demand video services) or by polling the human users. Thus, measuring QoE is expensive. What can be measured are the low-level network metrics (delay, jitter, SNR, etc). Then the question is, which is the relation between low-level metrics and QoE metrics? The idea is to create a data-set containing low-level and QoE metrics. Then and thanks to ML we model the relation between low-level and QoE metrics, this means that we understand that when delayK QoE levels are satisfactory. With this, operators know what low-level performance they need to target to offer good QoE. A nice relation is that once you establish the target performance of low-level metrics to achieve QoE, you can then use the 'QoS use-case' (or similar) to operate the network. Albert On Tue, Aug 8, 2017 at 7:02 PM, yanshen wrote: > Dear Albert, > > > > At least two supporters you have : ) > > > > I think that the QoS and QoE is just similar with my opinion mentioned > before that is the data can be divided into subjective and objective. Th= is > will be related with the data format and the way of obtaining. And your > case build up a bridge between the subjective and objective. > > > > Yansen > > > > > > *From:* =C3=96zg=C3=BC Alay [mailto:ozgu@simula.no] > *Sent:* Tuesday, August 08, 2017 2:02 PM > *To:* Albert Cabellos > *Cc:* yanshen ; idnet@ietf.org > *Subject:* Re: [Idnet] IDN dedicated session call for case > > > > Dear Albert, > > We are interested in this use case and will support the activities in thi= s > area. > > Best Regards, > > =C3=96zg=C3=BC > > > > On 8 August 2017 at 06:52, Albert Cabellos > wrote: > > Hi all > > > > Here=C2=B4s another use-case: > > > > Use case N+2: QoE > Description: Collect low-level metrics (SNR, latency, jitter, > losses, etc) and measure QoE. Then use ML to understand what is the > relation between satisfactory QoE and the low-level metrics. As an exampl= e > learn that when delay>N then QoE is degraded, but when M is satisfactory for the customers (please note that QoE cannot be measure= d > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > Process: 1. Low-level data collection and QoE measurement ; 2. > Training Model (input low-level metrics, output QoE); 3. Real-time data > capture and input; 4. Predict QoE; 5. Operate network to meet target QoE > requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, Jitter, > queue-size, etc > > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > > Kind regards > > > > Albert > > > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen wrote: > > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, for > applying AI into network management (NM), I=E2=80=99d try to list some Us= e Cases > and propose a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose your > focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external data > which may influence the traffic. Predict the traffic in short/long/specif= ic > term. Avoid the congestion or risk in previously. > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data capture and > input; 4. Predication output; 5. Fix error and go back to 3. > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might be > useful for some scenarios) > Service : [Service ID, Priority, ...] > (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic flows. > Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of physical/logical > port ); 2. Training Model; 3. Real-time data capture and input; 4. Output > percentages; 5. Fix error and go back to 3. > Data Format: Time : [Timestamp, Value type (Delay/Packet > Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope before Nov. > Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > --001a113f382ca7cdc30556490e2a Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi Yanshen

In my view they are not the = same use-case

- QoS use-case: As far as I understa= nd here you aim to configure the routing/switching infrastructure to achiev= e QoS for flows.

- QoE use-case: Here the idea is = that QoE can not typically be measured directly over the network, it has to= be measured either over the application (e.g., buffering in on-demand vide= o services) or by polling the human users. Thus, measuring QoE is expensive= .

What can be measured are the low-level network m= etrics (delay, jitter, SNR, etc). Then the question is, which is the relati= on between low-level metrics and QoE metrics? The idea is to create a data-= set containing low-level and QoE metrics. Then and thanks to ML we model th= e relation between low-level and QoE metrics, this means that we understand= that when delay<N, jitter<M and SNR>K QoE levels are satisfactory= . With this, operators know what low-level performance they need to target = to offer good QoE.=C2=A0

A nice relation is that o= nce you establish the target performance of low-level metrics to achieve Qo= E, you can then use the 'QoS use-case' (or similar) to operate the = network.

Albert=C2=A0

On Tue, Aug 8, 2017 at 7:02 PM, ya= nshen <yanshen@huawei.com> wrote:

Dear Albert,=

=C2=A0

At least two supporter= s you have : )

=C2=A0

I think that the QoS a= nd QoE is just similar with my opinion mentioned before that is the data ca= n be divided into subjective and objective.=C2=A0 This will be related with the data format and the way of obtaining. And your ca= se build up a bridge between the subjective and objective.

=C2=A0

Yansen

=C2=A0

=C2=A0

From: = =C3=96zg=C3=BC Alay [mailto:ozgu@simula.no]
Sent: Tuesday, August 08, 2017 2:02 PM
To: Albert Cabellos <albert.cabellos@gmail.com>
Cc: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: Re: [Idnet] IDN dedicated session call for case

=C2=A0

Dear Albert,=C2=A0

We are interested in this use c= ase and will support the activities in this area.

Best Regards,

=C3=96zg=C3=BC

=C2=A0

On 8 August 2017 at 06:52, Albe= rt Cabellos <albert.cabellos@gmail.com> wrote:

Hi all

=C2=A0

Here=C2=B4s another use-case:

=C2=A0

Use c= ase N+2: QoE
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Collect low-level metrics (SNR, la= tency, jitter, losses, etc) and measure QoE. Then use ML to understand what= is the relation between satisfactory QoE and the low-level metrics. As an = example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is satisfactory for the customers (pleas= e note that QoE cannot be measured directly over your network). This is use= ful to understand how the network must be operated to provide satisfactory = QoE.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Low-level data collection and QoE m= easurement ; 2. Training Model (input low-level metrics, output QoE); 3. Re= al-time data capture and input; 4. Predict QoE; 5. Operate network to meet = target QoE requirement, go to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Start, End, U= nit, Number of Value, Sampling Period]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Device ID, Port ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Direction: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Low-level metric : SNR, Delay, Jitte= r, queue-size, etc


=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

=C2=A0

Kind regards

= =C2=A0

Albert<= u>

=C2=A0

On Wed, Aug 2, 2017 at 7:12 PM,= yanshen <yanshe= n@huawei.com> wrote:

Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for applyin= g AI into network management (NM), I=E2=80=99d = try to list some Use Cases and propose a roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your focus= ed use cases or ideas.

Use case 1: Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Collect the history traffic data a= nd external data which may influence the traffic. Predict the traffic in sh= ort/long/specific term. Avoid the congestion or risk in previously.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data collection (e.g. traffic sampl= e of physical/logical port ); 2. Training Model; 3. Real-time data capture = and input; 4. Predication output; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Start, End, U= nit, Number of Value, Sampling Period]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Device ID, Port ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Direction: IN / OUT
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Route : [R1, R2, ..., RN]=C2=A0 (mig= ht be useful for some scenarios)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Service : [Service ID, Priority, ...= ]=C2=A0 (Not clear how to use it but seems useful)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Traffic: [T0, T1, T2, ..., TN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case 2: QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Description: Use multiple paths to distribute t= he traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.<= br> =C2=A0 =C2=A0 =C2=A0 =C2=A0 Process: 1. Data capture (e.g. traffic sample o= f physical/logical port ); 2. Training Model; 3. Real-time data capture and= input; 4. Output percentages; 5. Fix error and go back to 3.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Data Format:=C2=A0 =C2=A0 Time : [Timestamp, Va= lue type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period] =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Position: [Link ID, Device ID]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Value: [V0, V1, V2, ..., VN]
=C2=A0 =C2=A0 =C2=A0 =C2=A0 Message :=C2=A0 =C2=A0 =C2=A0 =C2=A0Request: as= k for the data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Reply: Data
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Notice: For notification or others =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and requi= rements
Sep. : Refining the cases and abstract the common elements
Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before Nov. H= ope that the content could become the draft of draft.

###Table of Content###
1. Gap and Requirement Analysis
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.1 Network Management requirement
=C2=A0 =C2=A0 =C2=A0 =C2=A0 1.2 TBD
2. Use Cases
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.1 Traffic Prediction
=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.2 QoS Management
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
3. Data Focus
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.1 Data attribute
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.2 Data format
=C2=A0 =C2=A0 =C2=A0 =C2=A0 3.3 TBD
4. Aims
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.1 Benchmarking Framework
=C2=A0 =C2=A0 =C2=A0 =C2=A0 4.2 TBD
###ToC End###


Yansen

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=C2=A0

=
_______________________________________________
IDNET mailing list
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=C2=A0


--001a113f382ca7cdc30556490e2a-- From nobody Tue Aug 8 21:07:37 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2F131131CF0 for ; Tue, 8 Aug 2017 21:07:36 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.221 X-Spam-Level: X-Spam-Status: No, score=-4.221 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id RFog9-al7UXf for ; Tue, 8 Aug 2017 21:07:33 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 3DE1E124B18 for ; Tue, 8 Aug 2017 21:07:32 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml705-cah.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DMF74185; Wed, 09 Aug 2017 04:07:30 +0000 (GMT) Received: from DGGEMM403-HUB.china.huawei.com (10.3.20.211) by lhreml705-cah.china.huawei.com (10.201.108.46) with Microsoft SMTP Server (TLS) id 14.3.301.0; Wed, 9 Aug 2017 05:07:29 +0100 Received: from DGGEMM505-MBS.china.huawei.com ([169.254.2.173]) by DGGEMM403-HUB.china.huawei.com ([10.3.20.211]) with mapi id 14.03.0301.000; Wed, 9 Aug 2017 12:07:23 +0800 From: yanshen To: Stenio Fernandes CC: "Diego R. Lopez" , Albert Cabellos , "idnet@ietf.org" , =?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Thread-Topic: [Idnet] IDN dedicated session call for case Thread-Index: AdMLcu+vuWBrdNuZQwG6l2oQPpJcKAETCHGAABTa8AAAAEJ2AAAAFQKAAAC+cIAAKpLegA== Date: Wed, 9 Aug 2017 04:07:22 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D297B362@DGGEMM505-MBS.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> <02682a50-626b-bd60-bf96-14748d1783e0@inria.fr> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020203.598A8A82.00C2, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, 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Q0luL1VGUEUNCj4gaHR0cDovL3d3dy5zdGVuaW9mZXJuYW5kZXMuY29tDQo= From yassine.hadjadj-aoul@irisa.fr Wed Aug 9 00:04:07 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DE1F9131EB2 for ; Wed, 9 Aug 2017 00:04:07 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.919 X-Spam-Level: X-Spam-Status: No, score=-6.919 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-5, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id E1LuZnkD6gPg for ; Wed, 9 Aug 2017 00:04:02 -0700 (PDT) Received: from mail3-relais-sop.national.inria.fr (mail3-relais-sop.national.inria.fr [192.134.164.104]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D83E0127010 for ; Wed, 9 Aug 2017 00:04:01 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,346,1498514400"; d="scan'208,217";a="234019347" Received: from vit35-1-78-219-79-119.fbx.proxad.net (HELO [192.168.0.49]) ([78.219.79.119]) by mail3-relais-sop.national.inria.fr with ESMTP/TLS/DHE-RSA-AES256-GCM-SHA384; 09 Aug 2017 09:03:59 +0200 From: Yassine HADJADJ-AOUL Message-Id: Content-Type: multipart/alternative; boundary="Apple-Mail=_44D4E87C-A9B9-4C03-93B3-B86E5BFA5506" Mime-Version: 1.0 (Mac OS X Mail 10.3 \(3273\)) Date: Wed, 9 Aug 2017 09:04:02 +0200 In-Reply-To: Cc: yanshen , "idnet@ietf.org" , =?utf-8?B?w5Z6Z8O8IEFsYXk=?= To: Albert Cabellos References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D297B223@DGGEMM505-MBS.china.huawei.com> X-Mailer: Apple Mail (2.3273) Archived-At: X-Mailman-Approved-At: Wed, 09 Aug 2017 00:09:44 -0700 Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 09 Aug 2017 07:08:07 -0000 --Apple-Mail=_44D4E87C-A9B9-4C03-93B3-B86E5BFA5506 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 Dear Albert, all, Please find some comments below. Yassine _______________________________________ Yassine HADJADJ AOUL Associate Professor, University of Rennes1 Dionysos Team / IRISA Lab Room: F334 Phone: +33 (0) 2 99 84 71 35 > Le 9 ao=C3=BBt 2017 =C3=A0 04:44, Albert Cabellos = a =C3=A9crit : >=20 > Hi Yanshen >=20 > In my view they are not the same use-case >=20 > - QoS use-case: As far as I understand here you aim to configure the = routing/switching infrastructure to achieve QoS for flows. >=20 > - QoE use-case: Here the idea is that QoE can not typically be = measured directly over the network, it has to be measured either over = the application (e.g., buffering in on-demand video services) or by = polling the human users. Thus, measuring QoE is expensive. That is true that to have a very good accuracy it is generally necessary = to have application-level measures (like the quantization parameter in a = video, or the playout interruptions). However, there is many papers in = the literature, some from our team, which demonstrate a good accuracy = for voice and video (UDP or RTP) using only network level parameters. We considered using Random Neural Networks in our developed tools =E2=80=A6= and we have ongoing work on that ... Concerning the cost, it is indeed expensive generally. However, there = are some techniques in the literature, which consist in doing the = learning with objective techniques (like VQM) =E2=80=A6 this eliminate = the need of users' panel =E2=80=A6=20 A former PhD student of our team had some contributions on that: No-reference Quality of Experience estimation of H264/SVC stream http://ieeexplore.ieee.org/document/6477778/ = >=20 > What can be measured are the low-level network metrics (delay, jitter, = SNR, etc). Then the question is, which is the relation between low-level = metrics and QoE metrics? The idea is to create a data-set containing = low-level and QoE metrics. Then and thanks to ML we model the relation = between low-level and QoE metrics, this means that we understand that = when delayK QoE levels are satisfactory. With this, = operators know what low-level performance they need to target to offer = good QoE.=20 The relation between QoS and QoE is generally non linear, so I believe = that in spite of finding a precise target we will have a function, which = may predict the QoE =E2=80=A6 and thus, we may decline some actions = accordingly. Yassine >=20 > A nice relation is that once you establish the target performance of = low-level metrics to achieve QoE, you can then use the 'QoS use-case' = (or similar) to operate the network. >=20 > Albert=20 >=20 > On Tue, Aug 8, 2017 at 7:02 PM, yanshen > wrote: > Dear Albert, >=20 > =20 >=20 > At least two supporters you have : ) >=20 > =20 >=20 > I think that the QoS and QoE is just similar with my opinion mentioned = before that is the data can be divided into subjective and objective. = This will be related with the data format and the way of obtaining. And = your case build up a bridge between the subjective and objective. >=20 > =20 >=20 > Yansen >=20 > =20 >=20 > =20 >=20 > From: =C3=96zg=C3=BC Alay [mailto:ozgu@simula.no = ]=20 > Sent: Tuesday, August 08, 2017 2:02 PM > To: Albert Cabellos > > Cc: yanshen >; = idnet@ietf.org > Subject: Re: [Idnet] IDN dedicated session call for case >=20 > =20 >=20 > Dear Albert,=20 >=20 > We are interested in this use case and will support the activities in = this area. >=20 > Best Regards, >=20 > =C3=96zg=C3=BC >=20 > =20 >=20 > On 8 August 2017 at 06:52, Albert Cabellos > wrote: >=20 > Hi all >=20 > =20 >=20 > Here=C2=B4s another use-case: >=20 > =20 >=20 > Use case N+2: QoE > Description: Collect low-level metrics (SNR, latency, jitter, = losses, etc) and measure QoE. Then use ML to understand what is the = relation between satisfactory QoE and the low-level metrics. As an = example learn that when delay>N then QoE is degraded, but when M Process: 1. Low-level data collection and QoE measurement ; 2. = Training Model (input low-level metrics, output QoE); 3. Real-time data = capture and input; 4. Predict QoE; 5. Operate network to meet target QoE = requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, = Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, Jitter, = queue-size, etc >=20 >=20 > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy >=20 > =20 >=20 > Kind regards >=20 > =20 >=20 > Albert >=20 > =20 >=20 > On Wed, Aug 2, 2017 at 7:12 PM, yanshen > wrote: >=20 > Dear all, >=20 > Since we plan to organize a dedicated session in NMRG, IETF100, for = applying AI into network management (NM), I=E2=80=99d try to list some = Use Cases and propose a roadmap and ToC before Nov. >=20 > These might be rough. You are welcome to refine them and propose your = focused use cases or ideas. >=20 > Use case 1: Traffic Prediction > Description: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in = short/long/specific term. Avoid the congestion or risk in previously. > Process: 1. Data collection (e.g. traffic sample of = physical/logical port ); 2. Training Model; 3. Real-time data capture = and input; 4. Predication output; 5. Fix error and go back to 3. > Data Format: Time : [Start, End, Unit, Number of Value, = Sampling Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might be = useful for some scenarios) > Service : [Service ID, Priority, ...] = (Not clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy >=20 > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic = flows. Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of = physical/logical port ); 2. Training Model; 3. Real-time data capture = and input; 4. Output percentages; 5. Fix error and go back to 3. > Data Format: Time : [Timestamp, Value type (Delay/Packet = Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy >=20 > Use case N: Waiting for your Ideas >=20 > Also I suggest a roadmap before Nov if possible. >=20 > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts and = requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, or = other key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### >=20 > A rough ToC is listed in following. We may take it as a scope before = Nov. Hope that the content could become the draft of draft. >=20 > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### >=20 >=20 > Yansen >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet = > =20 >=20 >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet = > =20 >=20 >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --Apple-Mail=_44D4E87C-A9B9-4C03-93B3-B86E5BFA5506 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 Dear Albert, all,

Please find some = comments below.

Yassine
_______________________________________
Yassine = HADJADJ AOUL

Associate Professor, = University of Rennes1
Dionysos Team / IRISA Lab
Room: F334
Phone: +33 (0) 2 99 84 71 35


Le 9 ao=C3=BBt 2017 =C3=A0 04:44, Albert Cabellos <albert.cabellos@gmail.com> a =C3=A9crit :

Hi Yanshen

In my view they are not the same use-case

- QoS use-case: As far = as I understand here you aim to configure the routing/switching = infrastructure to achieve QoS for flows.

- QoE use-case: Here the idea is that = QoE can not typically be measured directly over the network, it has to = be measured either over the application (e.g., buffering in on-demand = video services) or by polling the human users. Thus, measuring QoE is = expensive.

That is true that to have a very good accuracy it = is generally necessary to have application-level measures (like the = quantization parameter in a video, or the playout interruptions). = However, there is many papers in the literature, some from our team, = which demonstrate a good accuracy for voice and video (UDP or RTP) using = only network level parameters.

We = considered using Random Neural Networks in our developed tools =E2=80=A6 = and we have ongoing work on that ...

Concerning the cost, it is indeed expensive = generally. However, there are some techniques in the literature, which = consist in doing the learning with objective techniques (like VQM) =E2=80=A6= this eliminate the need of users' panel =E2=80=A6 

A former PhD student of our team had some = contributions on that:
No-reference Quality of Experience = estimation of H264/SVC stream



What can be measured are the low-level = network metrics (delay, jitter, SNR, etc). Then the question is, which = is the relation between low-level metrics and QoE metrics? The idea is = to create a data-set containing low-level and QoE metrics. Then and = thanks to ML we model the relation between low-level and QoE metrics, = this means that we understand that when delay<N, jitter<M and = SNR>K QoE levels are satisfactory. With this, operators know what = low-level performance they need to target to offer good = QoE. 

The relation between QoS and QoE is generally non = linear, so I believe that in spite of finding a precise target we will = have a function, which may predict the QoE =E2=80=A6 and thus, we may = decline some actions accordingly.

Yassine


A nice relation is that = once you establish the target performance of low-level metrics to = achieve QoE, you can then use the 'QoS use-case' (or similar) to operate = the network.

Albert 

On Tue, Aug 8, 2017 at 7:02 PM, = yanshen <yanshen@huawei.com> wrote:

Dear Albert,

 

At least two supporters you have : )

 

I think that the QoS and QoE is just similar with = my opinion mentioned before that is the data can be divided into = subjective and objective.  This will be related with the data format and the way of obtaining. And your = case build up a bridge between the subjective and objective.

 

Yansen

 

 

=

From: =C3=96zg=C3=BC Alay [mailto:ozgu@simula.no]
Sent: Tuesday, August 08, 2017 2:02 PM
To: Albert Cabellos <albert.cabellos@gmail.com>
Cc: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: Re: [Idnet] IDN dedicated session call for = case

 

Dear Albert, 

We = are interested in this use case and will support the activities in this = area.

Best Regards,

=C3=96zg=C3=BC

 

On = 8 August 2017 at 06:52, Albert Cabellos <albert.cabellos@gmail.com> wrote:

Hi = all

 

Here=C2=B4s another = use-case:

 

Use case N+2: QoE
        Description: Collect low-level metrics (SNR, = latency, jitter, losses, etc) and measure QoE. Then use ML to understand = what is the relation between satisfactory QoE and the low-level metrics. = As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is satisfactory for the customers = (please note that QoE cannot be measured directly over your network). = This is useful to understand how the network must be operated to provide = satisfactory QoE.
        Process: 1. Low-level data collection and = QoE measurement ; 2. Training Model (input low-level metrics, output = QoE); 3. Real-time data capture and input; 4. Predict QoE; 5. Operate = network to meet target QoE requirement, go to 3.
        Data Format:    Time : [Start, = End, Unit, Number of Value, Sampling Period]
                    =             Position: [Device ID, Port = ID]
                    =             Direction: IN / OUT
                    =             Low-level metric : SNR, Delay, = Jitter, queue-size, etc


        Message :       Request: = ask for the data
                    =             Reply: Data
                    =             Notice: For notification or = others
                    =             Policy: Control = policy

 

Kind regards

 

Albert

 

On = Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote:

Dear all,

Since we plan to organize a dedicated session in NMRG, IETF100, for = applying AI into network management (NM), I=E2=80=99d try to list some Use Cases and propose a = roadmap and ToC before Nov.

These might be rough. You are welcome to refine them and propose your = focused use cases or ideas.

Use case 1: Traffic Prediction
        Description: Collect the history traffic = data and external data which may influence the traffic. Predict the = traffic in short/long/specific term. Avoid the congestion or risk in = previously.
        Process: 1. Data collection (e.g. traffic = sample of physical/logical port ); 2. Training Model; 3. Real-time data = capture and input; 4. Predication output; 5. Fix error and go back to = 3.
        Data Format:    Time : [Start, = End, Unit, Number of Value, Sampling Period]
                    =             Position: [Device ID, Port = ID]
                    =             Direction: IN / OUT
                    =             Route : [R1, R2, ..., = RN]  (might be useful for some scenarios)
                    =             Service : [Service ID, = Priority, ...]  (Not clear how to use it but seems useful)
                    =             Traffic: [T0, T1, T2, ..., = TN]
        Message :       Request: = ask for the data
                    =             Reply: Data
                    =             Notice: For notification or = others
                    =             Policy: Control policy

Use case 2: QoS Management
        Description: Use multiple paths to = distribute the traffic flows. Adjust the percentages. Avoid congestion = and ensure QoS.
        Process: 1. Data capture (e.g. traffic = sample of physical/logical port ); 2. Training Model; 3. Real-time data = capture and input; 4. Output percentages; 5. Fix error and go back to = 3.
        Data Format:    Time : [Timestamp, = Value type (Delay/Packet Loss/...), Unit, Number of Value, Sampling = Period]
                    =             Position: [Link ID, Device = ID]
                    =             Value: [V0, V1, V2, ..., = VN]
        Message :       Request: = ask for the data
                    =             Reply: Data
                    =             Notice: For notification or = others
                    =             Policy: Control policy

Use case N: Waiting for your Ideas

Also I suggest a roadmap before Nov if possible.

### Roadmap ###
Aug. : Collecting the use cases (related with NM). Rough thoughts and = requirements
Sep. : Refining the cases and abstract the common elements
Oct. : Deeply analysis. Especially on Data Format, control flow, or = other key points
Nov.: F2F discussions on IETF100
### Roadmap End ###

A rough ToC is listed in following. We may take it as a scope before = Nov. Hope that the content could become the draft of draft.
=
###Table of Content###
1. Gap and Requirement Analysis
        1.1 Network Management requirement
        1.2 TBD
2. Use Cases
        2.1 Traffic Prediction
        2.2 QoS Management
        3.3 TBD
3. Data Focus
        3.1 Data attribute
        3.2 Data format
        3.3 TBD
4. Aims
        4.1 Benchmarking Framework
        4.2 TBD
###ToC End###


Yansen

_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

 


_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

 


_______________________________________________
IDNET = mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

= --Apple-Mail=_44D4E87C-A9B9-4C03-93B3-B86E5BFA5506-- From nobody Wed Aug 9 01:25:35 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2F60F127869 for ; Wed, 9 Aug 2017 01:25:34 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.92 X-Spam-Level: X-Spam-Status: No, score=-6.92 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_HI=-5, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id wEJLnx0UoNRw for ; Wed, 9 Aug 2017 01:25:31 -0700 (PDT) Received: from mail3-relais-sop.national.inria.fr (mail3-relais-sop.national.inria.fr [192.134.164.104]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 02C8C1270B4 for ; Wed, 9 Aug 2017 01:25:30 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,346,1498514400"; d="scan'208";a="234026131" Received: from unknown (HELO [192.168.1.108]) ([89.130.180.250]) by mail3-relais-sop.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 09 Aug 2017 10:25:28 +0200 To: Simone Ferlin , Stenio Fernandes References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> <02682a50-626b-bd60-bf96-14748d1783e0@inria.fr> Cc: yanshen , "idnet@ietf.org" , Albert Cabellos , "Diego R. Lopez" From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: Date: Wed, 9 Aug 2017 10:25:27 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 09 Aug 2017 08:25:34 -0000 Hi, There are many potential applications of trafic classification depending on how you want to classify, i.e. accroding to what criteria, e.g. types of applications, types of devices /OS (fingerprinting), anomalous/normal traffic, users profles... I presented in NMLRG last year some wokr related to HTTPS traffic classification (https://datatracker.ietf.org/doc/slides-95-nmlrg-7/). jerome Le 09/08/2017 =C3=A0 04:28, Simone Ferlin a =C3=A9crit : > Dear Jerome, > > Very interesting use-case, +1 support. I have interest in such > activities for traffic classification, anomaly detection in particular > for encrypted traffic. > > >> On Wed, Aug 9, 2017 at 12:20 AM, Stenio Fernandes w= rote: >>> Hi Jerome, Diego, et al, >>> >>> Those are excellent use cases. I have some published work on applied >>> machine learning to computer networking problems, including flow-base= d >>> traffic classification. I think another use case would be applying >>> unsupervised learning techniques for anomaly detection. I can >>> elaborate further on this. >>> >>> Stenio >>> >>> On Tue, Aug 8, 2017 at 10:59 AM, J=C3=A9r=C3=B4me Fran=C3=A7ois >>> wrote: >>>> 100% agree with you. I was far from being exhaustive as traffic feat= ures may >>>> depend on types of traffic (kin of sub use cases) >>>> >>>> jerome >>>> >>>> Le 08/08/2017 =C3=A0 16:56, Diego R. Lopez a =C3=A9crit : >>>> >>>> Hi Jerome, >>>> >>>> >>>> >>>> Agreed. This is a use case we are very much interested in, and actua= lly >>>> working in it now. Just let me say we are trying to evaluate which a= re the >>>> significant features of the flow to perform a proper classification,= >>>> depending on the flow nature (TLS, DTLS, QUIC, IPsec=E2=80=A6), and = that would >>>> define the concrete data to be exchanged or stored. >>>> >>>> >>>> >>>> Be goode, >>>> >>>> >>>> >>>> -- >>>> >>>> "Esta vez no fallaremos, Doctor Infierno" >>>> >>>> >>>> >>>> Dr Diego R. Lopez >>>> >>>> Telefonica I+D >>>> >>>> http://people.tid.es/diego.lopez/ >>>> >>>> >>>> >>>> e-mail: diego.r.lopez@telefonica.com >>>> >>>> Tel: +34 913 129 041 >>>> >>>> Mobile: +34 682 051 091 >>>> >>>> ----------------------------------- >>>> >>>> >>>> >>>> On 8/8/2017, 16:49 , "IDNET on behalf of J=C3=A9r=C3=B4me Fran=C3=A7= ois" >>>> wrote= : >>>> >>>> >>>> >>>> Hi all, >>>> >>>> Here is another use case about traffic classification. >>>> >>>> Use case N+3: (encrypted) traffic classification >>>> >>>> Description: collect flow-level traffic metrics such as protocol= >>>> information but also meta metrics such as distribution of packet siz= es, >>>> inter-arrival times... Then use such information to label the trafic= with >>>> the underlying application assuming that the granularity of classifi= cation >>>> may vary (type of application, exact application name, version...) >>>> Process: 1. collect packet information 2. flow reassembly (using= >>>> directly flow format such as IPFIX might be possible but depends on = the type >>>> of traffic, e.g. extracting the TLS application data is useful for e= ncrypted >>>> traffic) 3. Collect application specific information (useful when ta= rgeting >>>> a single type of application) =3D out of network information 4. trai= n the >>>> model 5. Online or offline testing 4. Apply application level polici= es. >>>> Data Format: Time : [Start, End, Unit, Number of Value, Sampl= ing >>>> Period] >>>> Position: [Device ID, Port ID] >>>> Direction: IN / OUT >>>> Flow level metric: packet size >>>> distributions, number of packets, inter-arrival time distribution, >>>> (+ application specific knowledge := payload >>>> parsing) >>>> >>>> Message : Request: ask for the data >>>> Reply: Data >>>> Notice: For notification or others >>>> Policy: Control policy >>>> >>>> >>>> Best regards, >>>> jerome >>>> >>>> >>>> Le 08/08/2017 =C3=A0 06:52, Albert Cabellos a =C3=A9crit : >>>> >>>> Hi all >>>> >>>> >>>> >>>> Here=C2=B4s another use-case: >>>> >>>> >>>> >>>> Use case N+2: QoE >>>> Description: Collect low-level metrics (SNR, latency, jitter= , >>>> losses, etc) and measure QoE. Then use ML to understand what is the = relation >>>> between satisfactory QoE and the low-level metrics. As an example le= arn that >>>> when delay>N then QoE is degraded, but when M>>> satisfactory for the customers (please note that QoE cannot be measu= red >>>> directly over your network). This is useful to understand how the ne= twork >>>> must be operated to provide satisfactory QoE. >>>> Process: 1. Low-level data collection and QoE measurement ; = 2. >>>> Training Model (input low-level metrics, output QoE); 3. Real-time d= ata >>>> capture and input; 4. Predict QoE; 5. Operate network to meet target= QoE >>>> requirement, go to 3. >>>> Data Format: Time : [Start, End, Unit, Number of Value, S= ampling >>>> Period] >>>> Position: [Device ID, Port ID] >>>> Direction: IN / OUT >>>> Low-level metric : SNR, Delay, Jitte= r, >>>> queue-size, etc >>>> >>>> >>>> Message : Request: ask for the data >>>> Reply: Data >>>> Notice: For notification or others >>>> Policy: Control policy >>>> >>>> >>>> >>>> Kind regards >>>> >>>> >>>> >>>> Albert >>>> >>>> >>>> >>>> On Wed, Aug 2, 2017 at 7:12 PM, yanshen wrote: >>>> >>>> Dear all, >>>> >>>> Since we plan to organize a dedicated session in NMRG, IETF100, for = applying >>>> AI into network management (NM), I=E2=80=99d try to list some Use Ca= ses and propose >>>> a roadmap and ToC before Nov. >>>> >>>> These might be rough. You are welcome to refine them and propose you= r >>>> focused use cases or ideas. >>>> >>>> Use case 1: Traffic Prediction >>>> Description: Collect the history traffic data and external d= ata >>>> which may influence the traffic. Predict the traffic in short/long/s= pecific >>>> term. Avoid the congestion or risk in previously. >>>> Process: 1. Data collection (e.g. traffic sample of physical= /logical >>>> port ); 2. Training Model; 3. Real-time data capture and input; 4. >>>> Predication output; 5. Fix error and go back to 3. >>>> Data Format: Time : [Start, End, Unit, Number of Value, S= ampling >>>> Period] >>>> Position: [Device ID, Port ID] >>>> Direction: IN / OUT >>>> Route : [R1, R2, ..., RN] (might be= useful >>>> for some scenarios) >>>> Service : [Service ID, Priority, ...= ] (Not >>>> clear how to use it but seems useful) >>>> Traffic: [T0, T1, T2, ..., TN] >>>> Message : Request: ask for the data >>>> Reply: Data >>>> Notice: For notification or others >>>> Policy: Control policy >>>> >>>> Use case 2: QoS Management >>>> Description: Use multiple paths to distribute the traffic fl= ows. >>>> Adjust the percentages. Avoid congestion and ensure QoS. >>>> Process: 1. Data capture (e.g. traffic sample of physical/lo= gical >>>> port ); 2. Training Model; 3. Real-time data capture and input; 4. O= utput >>>> percentages; 5. Fix error and go back to 3. >>>> Data Format: Time : [Timestamp, Value type (Delay/Packet >>>> Loss/...), Unit, Number of Value, Sampling Period] >>>> Position: [Link ID, Device ID] >>>> Value: [V0, V1, V2, ..., VN] >>>> Message : Request: ask for the data >>>> Reply: Data >>>> Notice: For notification or others >>>> Policy: Control policy >>>> >>>> Use case N: Waiting for your Ideas >>>> >>>> Also I suggest a roadmap before Nov if possible. >>>> >>>> ### Roadmap ### >>>> Aug. : Collecting the use cases (related with NM). Rough thoughts an= d >>>> requirements >>>> Sep. : Refining the cases and abstract the common elements >>>> Oct. : Deeply analysis. Especially on Data Format, control flow, or = other >>>> key points >>>> Nov.: F2F discussions on IETF100 >>>> ### Roadmap End ### >>>> >>>> A rough ToC is listed in following. We may take it as a scope before= Nov. >>>> Hope that the content could become the draft of draft. >>>> >>>> ###Table of Content### >>>> 1. Gap and Requirement Analysis >>>> 1.1 Network Management requirement >>>> 1.2 TBD >>>> 2. Use Cases >>>> 2.1 Traffic Prediction >>>> 2.2 QoS Management >>>> 3.3 TBD >>>> 3. Data Focus >>>> 3.1 Data attribute >>>> 3.2 Data format >>>> 3.3 TBD >>>> 4. Aims >>>> 4.1 Benchmarking Framework >>>> 4.2 TBD >>>> ###ToC End### >>>> >>>> >>>> Yansen >>>> >>>> _______________________________________________ >>>> IDNET mailing list >>>> IDNET@ietf.org >>>> https://www.ietf.org/mailman/listinfo/idnet >>>> >>>> >>>> >>>> >>>> >>>> >>>> _______________________________________________ >>>> >>>> IDNET mailing list >>>> >>>> IDNET@ietf.org >>>> >>>> https://www.ietf.org/mailman/listinfo/idnet >>>> >>>> >>>> >>>> >>>> ________________________________ >>>> >>>> Este mensaje y sus adjuntos se dirigen exclusivamente a su destinata= rio, >>>> puede contener informaci=C3=B3n privilegiada o confidencial y es par= a uso >>>> exclusivo de la persona o entidad de destino. Si no es usted. el >>>> destinatario indicado, queda notificado de que la lectura, utilizaci= =C3=B3n, >>>> divulgaci=C3=B3n y/o copia sin autorizaci=C3=B3n puede estar prohibi= da en virtud de la >>>> legislaci=C3=B3n vigente. Si ha recibido este mensaje por error, le = rogamos que >>>> nos lo comunique inmediatamente por esta misma v=C3=ADa y proceda a = su >>>> destrucci=C3=B3n. >>>> >>>> The information contained in this transmission is privileged and >>>> confidential information intended only for the use of the individual= or >>>> entity named above. If the reader of this message is not the intende= d >>>> recipient, you are hereby notified that any dissemination, distribut= ion or >>>> copying of this communication is strictly prohibited. If you have re= ceived >>>> this transmission in error, do not read it. Please immediately reply= to the >>>> sender that you have received this communication in error and then d= elete >>>> it. >>>> >>>> Esta mensagem e seus anexos se dirigem exclusivamente ao seu destina= t=C3=A1rio, >>>> pode conter informa=C3=A7=C3=A3o privilegiada ou confidencial e =C3=A9= para uso exclusivo >>>> da pessoa ou entidade de destino. Se n=C3=A3o =C3=A9 vossa senhoria = o destinat=C3=A1rio >>>> indicado, fica notificado de que a leitura, utiliza=C3=A7=C3=A3o, di= vulga=C3=A7=C3=A3o e/ou >>>> c=C3=B3pia sem autoriza=C3=A7=C3=A3o pode estar proibida em virtude = da legisla=C3=A7=C3=A3o vigente. >>>> Se recebeu esta mensagem por erro, rogamos-lhe que nos o comunique >>>> imediatamente por esta mesma via e proceda a sua destrui=C3=A7=C3=A3= o >>>> >>>> >>>> >>>> _______________________________________________ >>>> IDNET mailing list >>>> IDNET@ietf.org >>>> https://www.ietf.org/mailman/listinfo/idnet >>>> >>> >>> >>> -- >>> Prof. Stenio Fernandes >>> CIn/UFPE >>> http://www.steniofernandes.com >>> >>> _______________________________________________ >>> IDNET mailing list >>> IDNET@ietf.org >>> https://www.ietf.org/mailman/listinfo/idnet From nobody Wed Aug 9 02:37:56 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com 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autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id KfchIP5TVx2P for ; Wed, 9 Aug 2017 19:55:17 -0700 (PDT) Received: from ms.etri.re.kr (mscreen.etri.re.kr [129.254.9.16]) by ietfa.amsl.com (Postfix) with ESMTP id 6BA5B132529 for ; Wed, 9 Aug 2017 19:55:14 -0700 (PDT) Received: from unknown (HELO smtpeg.etri.re.kr) (129.254.27.142) by 129.254.9.16 with ESMTP; 10 Aug 2017 11:55:07 +0900 X-Original-SENDERIP: 129.254.27.142 X-Original-MAILFROM: mskim16@etri.re.kr X-Original-RCPTTO: yanshen@huawei.com, idnet@ietf.org, jerome.francois@inria.fr, albert.cabellos@gmail.com Received: from SMTP2.etri.info (129.254.28.72) by SMTPEG2.etri.info (129.254.27.142) with Microsoft SMTP Server (TLS) id 14.3.319.2; Thu, 10 Aug 2017 11:55:12 +0900 Received: from SMTP1.etri.info ([169.254.1.45]) by SMTP2.etri.info ([10.2.6.31]) with mapi id 14.03.0319.002; Thu, 10 Aug 2017 11:55:10 +0900 From: =?utf-8?B?6rmA66+87ISd?= To: =?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , "Albert Cabellos" , yanshen CC: "idnet@ietf.org" Thread-Topic: [Idnet] IDN dedicated session call for case Thread-Index: AdMLcu+vuWBrdNuZQwG6l2oQPpJcKAEQ8ACAABTa8AAAXmPvbA== Date: Thu, 10 Aug 2017 02:55:09 +0000 Message-ID: <5BC916BD50F92F45870ABA46212CB29C019C7592@SMTP1.etri.info> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> , <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> In-Reply-To: <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> Accept-Language: ko-KR, en-US Content-Language: ko-KR X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [129.254.28.41] Content-Type: multipart/alternative; boundary="_000_5BC916BD50F92F45870ABA46212CB29C019C7592SMTP1etriinfo_" MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " 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Cabellos CC: "idnet@ietf.org" , =?utf-8?B?w5Z6Z8O8IEFsYXk=?= Thread-Topic: [Idnet] IDN dedicated session call for case Thread-Index: AdMLcu+vuWBrdNuZQwG6l2oQPpJcKAETCHGAAAJqQAAAGLXWcABXMk3A Date: Thu, 10 Aug 2017 03:38:16 +0000 Message-ID: <3B110B81B721B940871EC78F107D848CFB04C9@DGGEMM506-MBS.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D297B223@DGGEMM505-MBS.china.huawei.com> In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D297B223@DGGEMM505-MBS.china.huawei.com> Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.134.134.227] Content-Type: multipart/alternative; boundary="_000_3B110B81B721B940871EC78F107D848CFB04C9DGGEMM506MBSchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090201.598BD533.0006, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.4.222, 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aXY+DQo8L2Rpdj4NCjwvZGl2Pg0KPC9ib2R5Pg0KPC9odG1sPg0K --_000_3B110B81B721B940871EC78F107D848CFB04C9DGGEMM506MBSchina_-- From nobody Thu Aug 10 05:40:23 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 7D476127868 for ; Thu, 10 Aug 2017 05:40:21 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.219 X-Spam-Level: X-Spam-Status: No, score=-4.219 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 8VAJMwj3rrfH for ; Thu, 10 Aug 2017 05:40:18 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with 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Message-ID: <6AE399511121AB42A34ACEF7BF25B4D297B7A9@DGGEMM505-MBS.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> , <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <5BC916BD50F92F45870ABA46212CB29C019C7592@SMTP1.etri.info> In-Reply-To: <5BC916BD50F92F45870ABA46212CB29C019C7592@SMTP1.etri.info> Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: multipart/alternative; boundary="_000_6AE399511121AB42A34ACEF7BF25B4D297B7A9DGGEMM505MBSchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A0B0205.598C542F.0184, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.2.173, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: fd7621320db95814aa6d78c44afb19fc Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org 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=?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , "Albert Cabellos" CC: "idnet@ietf.org" Thread-Topic: [Idnet] IDN dedicated session call for case Thread-Index: AdMSPWjTcv+pwObPRjSWgk7/CvViTA== Date: Fri, 11 Aug 2017 01:39:06 +0000 Message-ID: <3B110B81B721B940871EC78F107D848CFB0641@DGGEMM506-MBS.china.huawei.com> Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.134.134.227] Content-Type: multipart/alternative; boundary="_000_3B110B81B721B940871EC78F107D848CFB0641DGGEMM506MBSchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A0B0201.598D0AC1.0007, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.4.222, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: c2153b24b2a1cfd76b1aac9c2a095ac2 Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 11 Aug 2017 01:39:19 -0000 --_000_3B110B81B721B940871EC78F107D848CFB0641DGGEMM506MBSchina_ Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 SGkgS2ltLA0KICAgICBJIHRoaW5rIGRlZXAgbGVhcm5pbmcgbWV0aG9kIGlzIHZlcnkgaG90IHRv ZGF5LCBhbmQgY291bGQgYmUgdXNlZCBpbiBzb2x2aW5nIG5ldHdvcmsgcHJvYmxlbXMuDQogICAg IEZpcnN0LCBsZXTigJlzIHJlY2FwIHdoYXQgaXMgdGhlIGVzc2VuY2Ugb2YgZGVlcCBsZWFybmlu ZyBtZXRob2QsIHdoYXQga2luZCBvZiBzY2VuYXJpbyBpdCBiZXN0IGZpdCBpbj8gIFRoZSBiZXN0 IGltcGxlbWVudGF0aW9uIG9mIGRlZXAgbGVhcm5pbmcgaXMgcGVyZm9ybSBpbWFnZSByZWNvZ25p dGlvbi4gSXQgdGFrZSByYXcgaW1hZ2UgYXMgdGhlIGlucHV0IG9mIGRlZXAgbGVhcm5pbmcgbW9k ZWwgKGVnLiBDTk4pLCBhbmQgc2VsZWN0IHRoZSByZWNvbnN0cnVjdGl2ZSBmZWF0dXJlcyB0byBk byBpbWFnZSByZWNvZ25pdGlvbi4gSXQgc2hvdWxkIGJlIG5vdGVkIHRoYXQgdGhlIGZlYXR1cmUg 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amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.9 X-Spam-Level: X-Spam-Status: No, score=-1.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id koBofQhjs9eg for ; Thu, 10 Aug 2017 19:41:33 -0700 (PDT) Received: from ms.etri.re.kr (mscreen.etri.re.kr [129.254.9.16]) by ietfa.amsl.com (Postfix) with ESMTP id 28EE3132487 for ; Thu, 10 Aug 2017 19:41:31 -0700 (PDT) Received: from unknown (HELO smtpeg.etri.re.kr) (129.254.27.142) by 129.254.9.16 with ESMTP; 11 Aug 2017 11:41:25 +0900 X-Original-SENDERIP: 129.254.27.142 X-Original-MAILFROM: mskim16@etri.re.kr X-Original-RCPTTO: yanshen@huawei.com, idnet@ietf.org, albert.cabellos@gmail.com, haoyu.song@huawei.com, jerome.francois@inria.fr Received: from SMTP3.etri.info (129.254.28.73) by SMTPEG2.etri.info (129.254.27.142) with Microsoft SMTP Server (TLS) id 14.3.319.2; Fri, 11 Aug 2017 11:41:31 +0900 Received: from SMTP1.etri.info ([169.254.1.45]) by SMTP3.etri.info ([10.2.6.32]) with mapi id 14.03.0319.002; Fri, 11 Aug 2017 11:41:26 +0900 From: =?utf-8?B?6rmA66+87ISd?= To: Haoyu song , =?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , Albert Cabellos , yanshen CC: "idnet@ietf.org" Thread-Topic: [Idnet] IDN dedicated session call for case Thread-Index: AdMLcu+vuWBrdNuZQwG6l2oQPpJcKAEQ8ACAABTa8AAAXmPvbAAPhe4AACI8uKU= Date: Fri, 11 Aug 2017 02:41:25 +0000 Message-ID: <5BC916BD50F92F45870ABA46212CB29C019C78BE@SMTP1.etri.info> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> , <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <5BC916BD50F92F45870ABA46212CB29C019C7592@SMTP1.etri.info>, <78A2745BE9B57D4F9D27F86655EB87F925990540@SJCEML703-CHM.china.huawei.com> In-Reply-To: <78A2745BE9B57D4F9D27F86655EB87F925990540@SJCEML703-CHM.china.huawei.com> Accept-Language: ko-KR, en-US Content-Language: ko-KR 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-0700 (PDT) Received: from mail2-relais-roc.national.inria.fr (mail2-relais-roc.national.inria.fr [192.134.164.83]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id C99B9132523 for ; Thu, 10 Aug 2017 23:54:54 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.41,356,1498514400"; d="scan'208,217";a="286749230" Received: from unknown (HELO [192.168.1.108]) ([89.130.180.250]) by mail2-relais-roc.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 11 Aug 2017 08:54:49 +0200 To: idnet@ietf.org References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <5BC916BD50F92F45870ABA46212CB29C019C7592@SMTP1.etri.info> <6AE399511121AB42A34ACEF7BF25B4D297B7A9@DGGEMM505-MBS.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29C019C7857@SMTP1.etri.info> From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: <28ab908b-304f-fa8b-1fcc-df05b0f3105c@inria.fr> Date: Fri, 11 Aug 2017 08:54:48 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <5BC916BD50F92F45870ABA46212CB29C019C7857@SMTP1.etri.info> Content-Type: multipart/alternative; boundary="------------06A3BDB0472562678ED7A091" Archived-At: Subject: Re: [Idnet] IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 11 Aug 2017 06:55:00 -0000 This is a multi-part message in MIME format. --------------06A3BDB0472562678ED7A091 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hi, As you said, there might be several algorithms or techniques to be used in ML problems. However, I understand from the first use case description that use case description should be independent of the ML algorithm as much as possible= =2E Otherwise, we will mutliply the number of use cases. jerome Le 11/08/2017 =C3=A0 04:32, =EA=B9=80=EB=AF=BC=EC=84=9D a =C3=A9crit : > > Hi Yansen, > > > Thank you for check my usecase. > > > I know that the usecase is similar topic witht Jerome's one. > > However, I'm focusing on creative dataset for ML-based model. > We already discussed dataset applying of learning process for a > network architecture in last IETF side meeting, but we lost some > of points that pre-processing data to apply ML-based learning model is > needed with much more efforts. Especially, in trendy deep > learning models such as CNN & RNN, cretive dataset is a significant > part for efficiently deciding and making system performance. As many > guys knows, traffic classification using classical ML algorithms such > as anomaly detection or random decision forest had discussed in last > NMLRG so that we need more hot trendy issues in aspect of new network > machine learning. > > > Acually, our team is developing real time deep learning model for > traffic classification and makes an effort of pre-processing to create > ml dataset to apply a couple of deep models. In case of CNN, we > collect features for information of applications in payload, then > transfer it as like an image[MxN] of dataset. We have another approach > of pre-processing of RNN that we are collecting specific patterns from > # of packets per application. We also consider a few different methods > of ml-based pre-processing for deep learning models in a network > achitecture. > > > If possible, we should set of a new usecase that how ml-based dataset > for deep learning models are created by pre-processing in a network > architecture. > > > Best,=20 > > =20 > > Min-Suk Kim > =20 > Senior Researcher / Ph.D. > =20 > > =20 > > =20 > > -----------------------------------------------------------------------= - > *=EB=B3=B4=EB=82=B8 =EC=82=AC=EB=9E=8C : *"yanshen" > *=EB=B3=B4=EB=82=B8 =EB=82=A0=EC=A7=9C : *2017-08-10 21:40:15 ( +09:00 = ) > *=EB=B0=9B=EB=8A=94 =EC=82=AC=EB=9E=8C : *=EA=B9=80=EB=AF=BC=EC=84=9D <= mskim16@etri.re.kr> > *=EC=B0=B8=EC=A1=B0 : *idnet@ietf.org , J=C3=A9r=C3=B4m= e Fran=C3=A7ois > > *=EC=A0=9C=EB=AA=A9 : *RE: [Idnet] IDN dedicated session call for case > > =20 > > Hi Kim, > > =20 > > Thanks for your case in advance. > > =20 > > BTW, have you ever check the one that Jerome mentioned on Tuesday? It > is also a traffic classification case. > > =20 > > Apologized that I have no more insight in this area. What is the > difference between these two? > > =20 > > At least, whatever, this topic is high focused in current. > > =20 > > Yansen > > =20 > > *From:*=EA=B9=80=EB=AF=BC=EC=84=9D[mailto:mskim16@etri.re.kr] > > =20 > > *Sent:* Thursday, August 10, 2017 10:55 AM > > =20 > > *To:* J=C3=A9r=C3=B4me Fran=C3=A7ois ; Albert= Cabellos > ; yanshen > > =20 > > *Cc:* idnet@ietf.org > > =20 > > *Subject:* RE: [Idnet] IDN dedicated session call for case > > =20 > > HI, > > =20 > > We have an use-case for this: > > =20 > > Use case n+4: Real time traffic classfication using deep learning > > =20 > > Description: continuously collect packet data, then applying learning > process for traffic classification with generating application using > deep learning models such as CNN (convolutional neural network) and > RNN (recurrent neural network). Data-set to apply into the models are > generated by propecessing with features of information from flow in > packet data. > > =20 > > process: 1. collect packet data in real-time, 2. preprocessing > data-set for deep learning models, 3. Training model using deep > learning (CNN & RNN), 4. On-line data learning & classifying 5. > Monitoring and analyzing traffic in the web=20 > > =20 > > Data Format: Time : [Start, End, Unit, Number of Value, Sampling Period= ] > > =20 > > Position: [Device ID, Port ID] > > =20 > > Direction: IN / OUT > > =20 > > Flow level metric: packet & flow size, > number of packet(RNN), payload parsing > > =20 > > Message: Request: ask for the data > > =20 > > Reply: Data > > =20 > > Notice: For notification or others > > =20 > > Policy: Control policy > > =20 > > Regards, > > =20 > > Min-Suk Kim > > =20 > > Senior Researcher / Ph.D. > > =20 > > =20 > > =20 > > =20 > > =20 > > -----------------------------------------------------------------------= - > > *=EB=B3=B4=EB=82=B8 =EC=82=AC=EB=9E=8C: *"J=C3=A9r=C3=B4me Fran=C3=A7oi= s" > > > *=EB=B3=B4=EB=82=B8 =EB=82=A0=EC=A7=9C: *2017-08-08 23:49:47 ( +09:00 )= > > *=EB=B0=9B=EB=8A=94 =EC=82=AC=EB=9E=8C: *Albert Cabellos >, yanshen > > > *=EC=B0=B8=EC=A1=B0: *idnet@ietf.org > > > *=EC=A0=9C=EB=AA=A9: *Re: [Idnet] IDN dedicated session call for case > > =20 > > Hi all, > > =20 > > =20 > > Here is another use case about traffic classification. > > =20 > > =20 > > Use case N+3: (encrypted) traffic classification > > =20 > > =20 > > Description: collect flow-level traffic metrics such as protocol > information but also meta metrics such as distribution of packet > sizes, inter-arrival times... Then use such information to label the > trafic with the underlying application assuming that the granularity > of classification may vary (type of application, exact application > name, version...) > > =20 > > Process: 1. collect packet information 2. flow reassembly (using > directly flow format such as IPFIX might be possible but depends on > the type of traffic, e.g. extracting the TLS application data is > useful for encrypted traffic) 3. Collect application specific > information (useful when targeting a single type of application) =3D ou= t > of network information 4. train the model 5. Online or offline testing > 4. Apply application level policies. > > =20 > > Data Format: Time : [Start, End, Unit, Number of Value, > Sampling Period] > > =20 > > Position: [Device ID, Port ID] > > =20 > > Direction: IN / OUT > > =20 > > Flow level metric: packet size > distributions, number of packets, inter-arrival time distribution, > > =20 > > (+ application specific knowledge : > payload parsing) > > =20 > > =20 > > Message : Request: ask for the data > > =20 > > Reply: Data > > =20 > > Notice: For notification or others > > =20 > > Policy: Control policy > > =20 > > =20 > > =20 > > Best regards, > > =20 > > jerome > > =20 > > =20 > > =20 > > Le 08/08/2017 =C3=A006:52, Albert Cabellos a =C3=A9crit : > > =20 > > Hi all > > =20 > > Here=C2=B4s another use-case: > > =20 > > Use case N+2: QoE > > =20 > > style=3D"font-size: 12px;"> Description: Collect low-level > metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then > use ML to understand what is the relation between satisfactory QoE > and the low-level metrics. As an example learn that when delay>N > then QoE is degraded, but when M for the customers (please note that QoE cannot be measured > directly over your network). This is useful to understand how the > network must be operated to provide satisfactory QoE. > > =20 > > style=3D"font-size: 12px;"> Process: 1. Low-level data > collection and QoE measurement ; 2. Training Model (input > low-level metrics, output QoE); 3. Real-time data capture and > input; 4. Predict QoE; 5. Operate network to meet target QoE > requirement, go to 3. > > =20 > > style=3D"font-size: 12px;"> Data Format: Time : [Start, > End, Unit, Number of Value, Sampling Period] > > =20 > > style=3D"font-size: 12px;"> Position= : > [Device ID, Port ID] > > =20 > > style=3D"font-size: 12px;"> =20 > Direction: IN / OUT > > =20 > > style=3D"font-size: 12px;"> Low-leve= l > metric : SNR, Delay, Jitter, queue-size, etc > > =20 > > style=3D"font-size: 12px;"> Message : Request: ask for= > the data > > =20 > > style=3D"font-size: 12px;"> Reply: D= ata > > =20 > > style=3D"font-size: 12px;"> Notice: > For notification or others > > =20 > > style=3D"font-size: 12px;"> Policy: > Control policy > > =20 > > =20 > > Kind regards > > =20 > > Albert > > =20 > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen > wrote: > > =20 > > Dear all, > > =20 > > =20 > > Since we plan to organize a dedicated session in NMRG, > IETF100, for applying AI into network management (NM), I=E2=80=99= d try > to list some Use Cases and propose a roadmap and ToC before Nov= =2E > > =20 > > =20 > > These might be rough. You are welcome to refine them and > propose your focused use cases or ideas. > > =20 > > =20 > > Use case 1: Traffic Prediction > > =20 > > Description: Collect the history traffic data and > external data which may influence the traffic. Predict the > traffic in short/long/specific term. Avoid the congestion or > risk in previously. > > =20 > > Process: 1. Data collection (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Predication output; 5. Fix error and go > back to 3. > > =20 > > Data Format: Time : [Start, End, Unit, Number of > Value, Sampling Period] > > =20 > > Position: [Device ID, Port ID] > > =20 > > Direction: IN / OUT > > =20 > > Route : [R1, R2, ..., RN]=20 > (might be useful for some scenarios) > > =20 > > Service : [Service ID, > Priority, ...] (Not clear how to use it but seems useful) > > =20 > > Traffic: [T0, T1, T2, ..., TN] > > =20 > > Message : Request: ask for the data > > =20 > > Reply: Data > > =20 > > Notice: For notification or > others > > =20 > > Policy: Control policy > > =20 > > =20 > > Use case 2: QoS Management > > =20 > > Description: Use multiple paths to distribute the > traffic flows. Adjust the percentages. Avoid congestion and > ensure QoS. > > =20 > > Process: 1. Data capture (e.g. traffic sample of > physical/logical port ); 2. Training Model; 3. Real-time data > capture and input; 4. Output percentages; 5. Fix error and go > back to 3. > > =20 > > Data Format: Time : [Timestamp, Value type > (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period= ] > > =20 > > Position: [Link ID, Device ID] > > =20 > > Value: [V0, V1, V2, ..., VN] > > =20 > > Message : Request: ask for the data > > =20 > > Reply: Data > > =20 > > Notice: For notification or > others > > =20 > > Policy: Control policy > > =20 > > =20 > > Use case N: Waiting for your Ideas > > =20 > > =20 > > Also I suggest a roadmap before Nov if possible. > > =20 > > =20 > > ### Roadmap ### > > =20 > > Aug. : Collecting the use cases (related with NM). Rough > thoughts and requirements > > =20 > > Sep. : Refining the cases and abstract the common elements > > =20 > > Oct. : Deeply analysis. Especially on Data Format, control > flow, or other key points > > =20 > > Nov.: F2F discussions on IETF100 > > =20 > > ### Roadmap End ### > > =20 > > =20 > > A rough ToC is listed in following. We may take it as a scope > before Nov. Hope that the content could become the draft of > draft. > > =20 > > =20 > > ###Table of Content### > > =20 > > 1. Gap and Requirement Analysis > > =20 > > 1.1 Network Management requirement > > =20 > > 1.2 TBD > > =20 > > 2. Use Cases > > =20 > > 2.1 Traffic Prediction > > =20 > > 2.2 QoS Management > > =20 > > 3.3 TBD > > =20 > > 3. Data Focus > > =20 > > 3.1 Data attribute > > =20 > > 3.2 Data format > > =20 > > 3.3 TBD > > =20 > > 4. Aims > > =20 > > 4.1 Benchmarking Framework > > =20 > > 4.2 TBD > > =20 > > ###ToC End### > > =20 > > =20 > > =20 > > Yansen > > =20 > > =20 > > _______________________________________________ > > =20 > > IDNET mailing list > > =20 > > IDNET@ietf.org > > =20 > > https://www.ietf.org/mailman/listinfo/idnet > > =20 > > =20 > > =20 > > =20 > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > =20 > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --------------06A3BDB0472562678ED7A091 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: 8bit Hi,

As you said, there might be several algorithms or techniques to be used in ML problems.

However, I understand from the first use case description that use case description should be independent of the ML algorithm as much as possible.
Otherwise, we will mutliply the number of use cases.


jerome

Le 11/08/2017 à 04:32, ê¹€ë¯¼ì„ a écrit :

Hi Yansen,


Thank you for check my usecase.


I know that the usecase is similar topic witht Jerome's one.

However, I'm focusing on creative dataset for ML-based model. We already discussed dataset applying of learning process for a network architecture in last IETF side meeting, but we lost some of points that pre-processing data to apply ML-based learning model is needed with much more efforts. Especially, in trendy deep learning models such as CNN & RNN, cretive dataset is a significant part for efficiently deciding and making system performance. As many guys knows, traffic classification using classical ML algorithms such as anomaly detection or random decision forest had discussed in last NMLRG so that we need more hot trendy issues in aspect of new network machine learning.


Acually, our team is developing real time deep learning model for traffic classification and makes an effort of pre-processing to create ml dataset to apply a couple of deep models. In case of CNN, we collect features for information of applications in payload, then transfer it as like an image[MxN] of dataset. We have another approach of pre-processing of RNN that we are collecting specific patterns from # of packets per application. We also consider a few different methods of ml-based pre-processing for deep learning models in a network achitecture.


If possible, we should set of a new usecase that how ml-based dataset for deep learning models are created by pre-processing in a network architecture.


Best, 

 

Min-Suk Kim
 
Senior Researcher / Ph.D.
 

 

 

보낸 사람 : "yanshen" <yanshen@huawei.com>
보낸 날짜 : 2017-08-10 21:40:15 ( +09:00 )
받는 사람 : ê¹€ë¯¼ì„ <mskim16@etri.re.kr>
참조 : idnet@ietf.org <idnet@ietf.org>, Jérôme François <jerome.francois@inria.fr>
제목 : RE: [Idnet] IDN dedicated session call for case

 

Hi Kim,

 

Thanks for your case in advance.

 

BTW, have you ever check the one that Jerome mentioned on Tuesday? It is also a traffic classification case.

 

Apologized that I have no more insight in this area. What is the difference between these two?

 

At least, whatever, this topic is high focused in current.

 

Yansen

 

From: ê¹€ë¯¼ì„ [mailto:mskim16@etri.re.kr]

 

Sent: Thursday, August 10, 2017 10:55 AM

 

To: Jérôme François <jerome.francois@inria.fr>; Albert Cabellos <albert.cabellos@gmail.com>; yanshen <yanshen@huawei.com>

 

Cc: idnet@ietf.org

 

Subject: RE: [Idnet] IDN dedicated session call for case

 

HI,

 

We have an use-case for this:

 

Use case n+4: Real time traffic classfication using deep learning

 

Description: continuously collect packet data, then applying learning process for traffic classification with generating application using deep learning models such as CNN (convolutional neural network) and RNN (recurrent neural network). Data-set to apply into the models are generated by propecessing with features of information from flow in packet data.

 

process: 1. collect packet data in real-time, 2. preprocessing data-set for deep learning models, 3. Training model using deep learning (CNN & RNN), 4. On-line data learning & classifying 5. Monitoring and analyzing traffic in the web 

 

Data Format: Time : [Start, End, Unit, Number of Value, Sampling Period]

 

                            Position: [Device ID, Port ID]

 

                            Direction: IN / OUT

 

                            Flow level metric: packet & flow size, number of packet(RNN), payload parsing

 

 Message: Request: ask for the data

 

                          Reply: Data

 

                          Notice: For notification or others

 

                          Policy: Control policy

 

Regards,

 

Min-Suk Kim

 

Senior Researcher / Ph.D.

 

 

 

 

 

보낸 사람 : "Jérôme François" <jerome.francois@inria.fr>

보낸 날짜 : 2017-08-08 23:49:47 ( +09:00 )

받는 사람 : Albert Cabellos <albert.cabellos@gmail.com>, yanshen <yanshen@huawei.com>

참조 : idnet@ietf.org <idnet@ietf.org>

제목 : Re: [Idnet] IDN dedicated session call for case

 

Hi all,

 

 

Here is another use case about traffic classification.

 

 

Use case N+3: (encrypted) traffic classification

 

 

    Description: collect flow-level traffic metrics such as protocol information but also meta metrics such as distribution of packet sizes, inter-arrival times... Then use such information to label the trafic with the underlying application assuming that the granularity of classification may vary (type of application, exact application name, version...)

 

    Process: 1. collect packet information 2. flow reassembly (using directly flow format such as IPFIX might be possible but depends on the type of traffic, e.g. extracting the TLS application data is useful for encrypted traffic) 3. Collect application specific information (useful when targeting a single type of application) = out of network information 4. train the model 5. Online or offline testing 4. Apply application level policies.

 

    Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]

 

                                Position: [Device ID, Port ID]

 

                                Direction: IN / OUT

 

                                Flow level metric: packet size distributions, number of packets, inter-arrival time distribution,

 

                                 (+ application specific knowledge : payload parsing)

 

 

    Message :       Request: ask for the data

 

                           Reply: Data

 

                           Notice: For notification or others

 

                           Policy: Control policy

 

 

 

Best regards,

 

jerome

 

 

 

Le 08/08/2017 à 06:52, Albert Cabellos a écrit :

 

Hi all

 

Here´s another use-case:

 

Use case N+2: QoE

 

style="font-size: 12px;">        Description: Collect low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then use ML to understand what is the relation between satisfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is satisfactory for the customers (please note that QoE cannot be measured directly over your network). This is useful to understand how the network must be operated to provide satisfactory QoE.

 

style="font-size: 12px;">        Process: 1. Low-level data collection and QoE measurement ; 2. Training Model (input low-level metrics, output QoE); 3. Real-time data capture and input; 4. Predict QoE; 5. Operate network to meet target QoE requirement, go to 3.

 

style="font-size: 12px;">        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]

 

style="font-size: 12px;">                                Position: [Device ID, Port ID]

 

style="font-size: 12px;">                                Direction: IN / OUT

 

style="font-size: 12px;">                                Low-level metric : SNR, Delay, Jitter, queue-size, etc

 

style="font-size: 12px;">        Message :       Request: ask for the data

 

style="font-size: 12px;">                                Reply: Data

 

style="font-size: 12px;">                                Notice: For notification or others

 

style="font-size: 12px;">                                Policy: Control policy

 

 

Kind regards

 

Albert

 

On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote:

 

Dear all,

 

 

Since we plan to organize a dedicated session in NMRG, IETF100, for applying AI into network management (NM), I’d try to list some Use Cases and propose a roadmap and ToC before Nov.

 

 

These might be rough. You are welcome to refine them and propose your focused use cases or ideas.

 

 

Use case 1: Traffic Prediction

 

        Description: Collect the history traffic data and external data which may influence the traffic. Predict the traffic in short/long/specific term. Avoid the congestion or risk in previously.

 

        Process: 1. Data collection (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Predication output; 5. Fix error and go back to 3.

 

        Data Format:    Time : [Start, End, Unit, Number of Value, Sampling Period]

 

                                Position: [Device ID, Port ID]

 

                                Direction: IN / OUT

 

                                Route : [R1, R2, ..., RN]  (might be useful for some scenarios)

 

                                Service : [Service ID, Priority, ...]  (Not clear how to use it but seems useful)

 

                                Traffic: [T0, T1, T2, ..., TN]

 

        Message :       Request: ask for the data

 

                                Reply: Data

 

                                Notice: For notification or others

 

                                Policy: Control policy

 

 

Use case 2: QoS Management

 

        Description: Use multiple paths to distribute the traffic flows. Adjust the percentages. Avoid congestion and ensure QoS.

 

        Process: 1. Data capture (e.g. traffic sample of physical/logical port ); 2. Training Model; 3. Real-time data capture and input; 4. Output percentages; 5. Fix error and go back to 3.

 

        Data Format:    Time : [Timestamp, Value type (Delay/Packet Loss/...), Unit, Number of Value, Sampling Period]

 

                                Position: [Link ID, Device ID]

 

                                Value: [V0, V1, V2, ..., VN]

 

        Message :       Request: ask for the data

 

                                Reply: Data

 

                                Notice: For notification or others

 

                                Policy: Control policy

 

 

Use case N: Waiting for your Ideas

 

 

Also I suggest a roadmap before Nov if possible.

 

 

### Roadmap ###

 

Aug. : Collecting the use cases (related with NM). Rough thoughts and requirements

 

Sep. : Refining the cases and abstract the common elements

 

Oct. : Deeply analysis. Especially on Data Format, control flow, or other key points

 

Nov.: F2F discussions on IETF100

 

### Roadmap End ###

 

 

A rough ToC is listed in following. We may take it as a scope before Nov. Hope that the content could become the draft of draft.

 

 

###Table of Content###

 

1. Gap and Requirement Analysis

 

        1.1 Network Management requirement

 

        1.2 TBD

 

2. Use Cases

 

        2.1 Traffic Prediction

 

        2.2 QoS Management

 

        3.3 TBD

 

3. Data Focus

 

        3.1 Data attribute

 

        3.2 Data format

 

        3.3 TBD

 

4. Aims

 

        4.1 Benchmarking Framework

 

        4.2 TBD

 

###ToC End###

 

 

 

Yansen

 

 

_______________________________________________

 

IDNET mailing list

 

IDNET@ietf.org

 

https://www.ietf.org/mailman/listinfo/idnet

 

 

 

 

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13 Aug 2017 20:34:14 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 29EB412420B for ; Sun, 13 Aug 2017 20:34:14 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml701-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTJ12489; Mon, 14 Aug 2017 03:34:12 +0000 (GMT) Received: from DGGEMM404-HUB.china.huawei.com (10.3.20.212) by lhreml701-cah.china.huawei.com (10.201.108.42) with Microsoft SMTP Server (TLS) id 14.3.301.0; Mon, 14 Aug 2017 04:34:10 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.229]) by DGGEMM404-HUB.china.huawei.com ([10.3.20.212]) with mapi id 14.03.0301.000; Mon, 14 Aug 2017 11:34:04 +0800 From: yanshen To: Stenio Fernandes CC: "idnet@ietf.org" Thread-Topic: [Idnet] IDN dedicated session call for case Thread-Index: AdMLcu+vuWBrdNuZQwG6l2oQPpJcKAETCHGAABTa8AAAAEJ2AAAAFQKAAAC+cIABJc5t8A== Date: Mon, 14 Aug 2017 03:34:03 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D2982F1F@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D297A34A@DGGEMM505-MBS.china.huawei.com> <7e6d507a-e8bf-b334-e394-6dc08b4dc3b1@inria.fr> <051F18D1-621A-4BF7-94F6-3C2D243F39C8@telefonica.com> <02682a50-626b-bd60-bf96-14748d1783e0@inria.fr> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020205.59911A34.00A7, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.229, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 3015a774b72f5099abd8270ae413b54b Archived-At: Subject: Re: [Idnet] IDN 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aWJpZGEgZW0NCj4gdmlydHVkZSBkYSBsZWdpc2xhw6fDo28gdmlnZW50ZS4NCj4gPiBTZSByZWNl YmV1IGVzdGEgbWVuc2FnZW0gcG9yIGVycm8sIHJvZ2Ftb3MtbGhlIHF1ZSBub3MgbyBjb211bmlx dWUNCj4gPiBpbWVkaWF0YW1lbnRlIHBvciBlc3RhIG1lc21hIHZpYSBlIHByb2NlZGEgYSBzdWEg ZGVzdHJ1acOnw6NvDQo+ID4NCj4gPg0KPiA+DQo+ID4gX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX18NCj4gPiBJRE5FVCBtYWlsaW5nIGxpc3QNCj4gPiBJRE5F VEBpZXRmLm9yZw0KPiA+IGh0dHBzOi8vd3d3LmlldGYub3JnL21haWxtYW4vbGlzdGluZm8vaWRu ZXQNCj4gPg0KPiANCj4gDQo+IA0KPiAtLQ0KPiBQcm9mLiBTdGVuaW8gRmVybmFuZGVzDQo+IENJ bi9VRlBFDQo+IGh0dHA6Ly93d3cuc3RlbmlvZmVybmFuZGVzLmNvbQ0K From nobody Sun Aug 13 20:36:31 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id A21E9120713 for ; Sun, 13 Aug 2017 20:36:30 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 4EQLnDe-_pe9 for ; Sun, 13 Aug 2017 20:36:28 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 66FBA1200C5 for ; Sun, 13 Aug 2017 20:36:27 -0700 (PDT) Received: from 172.18.7.190 (EHLO LHREML711-CAH.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTJ12787; Mon, 14 Aug 2017 03:36:25 +0000 (GMT) Received: from DGGEMM403-HUB.china.huawei.com (10.3.20.211) by LHREML711-CAH.china.huawei.com (10.201.108.34) with Microsoft SMTP Server (TLS) id 14.3.301.0; Mon, 14 Aug 2017 04:36:24 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.229]) by DGGEMM403-HUB.china.huawei.com ([10.3.20.211]) with mapi id 14.03.0301.000; Mon, 14 Aug 2017 11:36:15 +0800 From: yanshen To: "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5Ykgmg== Date: Mon, 14 Aug 2017 03:36:15 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: multipart/alternative; boundary="_000_6AE399511121AB42A34ACEF7BF25B4D2982F34DGGEMM505MBXchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090202.59911ABA.0007, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.229, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 0e3e834d76cae89bffc59bb04bf857f4 Archived-At: Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 14 Aug 2017 03:36:31 -0000 --_000_6AE399511121AB42A34ACEF7BF25B4D2982F34DGGEMM505MBXchina_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M

Dear all,

 

Here is a summary and some inde= x (2017.08.14). Till now, whatever the case is supported or not, I tried to= organize all the content and keep the core part. It is still welcome to co= ntribute and discuss.

 

If I miss something important, = please let me know. Apologized in advance.

 

Yansen

 

 

---------  Roadmap  -= --------

***Aug. : Collecting the use ca= ses (related with NM). Rough thoughts and requirements

Sep. : Refining the cases and a= bstract the common elements

Oct. : Deeply analysis. Especia= lly on Data Format, control flow, or other key points

Nov.: F2F discussions on IETF10= 0

---------  Roadmap End &nb= sp;---------

 

 

1. Gap and Requirement Analysis=

    1.1 Network = Management requirement

    1.2 TBD=

2. Use Cases<= /p>

    2.1 Traffic = Prediction

     &= nbsp;           &nbs= p; Proposed by: yanshen@huawei.com

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

     &= nbsp;           &nbs= p; Abstract: Collect the history traffic data and external data which may i= nfluence the traffic. Predict the traffic in short/long/specific term. Avoi= d the congestion or risk in previously.

     &= nbsp;           &nbs= p;

    2.2 QoS Mana= gement

     &= nbsp;           &nbs= p; Proposed by: yanshen@huawei.com

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

     &= nbsp;           &nbs= p; Abstract: Use multiple paths to distribute the traffic flows. Adjust the= percentages. Avoid congestion and ensure QoS.

     &= nbsp;  

    2.3 App= lication (and/or DDoS) detection

     &= nbsp;           &nbs= p; Proposed by: aydinulas@gmx.net

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html=

     &= nbsp;           &nbs= p; Abstract: Detect the application (or attack) from network packets (HTTPS= or plain) Collect the history traffic data and identify a service or attac= k (ex: Skype, Viber, DDoS attack etc.)

     &= nbsp;  

     &= nbsp;   2.4 QoE Management

     &= nbsp;           &nbs= p; Proposed by: albert.cabellos@gmail.com

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html=

     &= nbsp;           &nbs= p; Abstract: Collect low-level metrics (SNR, latency, jitter, losses, etc) = and measure QoE. Then use ML to understand what is the relation between sat= isfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N = then QoE is satisfactory for the customers (please note that QoE cannot be = measured directly over your network). This is useful to understand how the = network must be operated to provide satisfactory QoE.

     &= nbsp;  

     &= nbsp;   2.5 (Encrypted) Traffic Classification<= /p>

     &= nbsp;           &nbs= p; Proposed by: jerome.francois@inria.fr; mskim16@etri.re.kr

     &= nbsp;           &nbs= p; Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg0= 0141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00153.html

     &= nbsp;           &nbs= p; Abstract:

     &= nbsp;           &nbs= p;          [Jerome] collect f= low-level traffic metrics such as protocol information but also meta metric= s such as distribution of packet sizes, inter-arrival times... Then use suc= h information to label the traffic with the underlying application assuming that the granularity = of classification may vary (type of application, exact application name, ve= rsion...)

     &= nbsp;           &nbs= p;          [Min-Suk Kim] cont= inuously collect packet data, then applying learning process for traffic cl= assification with generating application using deep learning models such as= CNN (convolutional neural network) and RNN (recurrent neural network). Data-set to apply into the mo= dels are generated by processing with features of information from flow in = packet data.

     &= nbsp;           &nbs= p;

     &= nbsp;   2.6 TBD

     &= nbsp;  

3. Data Focus=

    3.1 Data att= ribute

    3.2 Data for= mat

    3.3 TBD=

     &= nbsp;  

4. Support Technologies

    4.1 Benchmar= king Framework

     &= nbsp;           &nbs= p; Proposed by: pedro@nict.go.jp

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html=

     &= nbsp;           &nbs= p; Abstract: A proper benchmarking framework comprises a set of reference p= rocedures, methods, and models that can (or better *must*) be followed to a= ssess the quality of an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more sp= ecific to the IDNET topics, is the inclusion, dependency, or just the gener= al relation of a standard format enforced to the data that is used (input) = and produced (output) by the framework, so a kind of "data market" can arise without requiring to transf= orm the data. The initial scope of input/output data would be the datasets,= but also the new knowledge items that are stated as a result of applying t= he benchmarking procedures defined by the framework, which can be collected together to build a database of benchmar= k results, or just contrasted with other existing entries in the database t= o know the position of the solution just evaluated. This increases the usef= ulness of IDNET.

     &= nbsp;  

    4.2 TBD=

--_000_6AE399511121AB42A34ACEF7BF25B4D2982F34DGGEMM505MBXchina_-- From nobody Mon Aug 14 09:47:52 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 20DF61323A5 for ; Mon, 14 Aug 2017 09:47:51 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id TjJB43YbLPyv for ; Mon, 14 Aug 2017 09:47:47 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 2A1B21323AE for ; Mon, 14 Aug 2017 09:47:47 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml705-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTK43199; Mon, 14 Aug 2017 16:47:44 +0000 (GMT) Received: from SJCEML703-CHM.china.huawei.com (10.208.112.39) by lhreml705-cah.china.huawei.com (10.201.108.46) with Microsoft SMTP Server (TLS) id 14.3.301.0; Mon, 14 Aug 2017 17:47:44 +0100 Received: from SJCEML701-CHM.china.huawei.com ([169.254.3.191]) by SJCEML703-CHM.china.huawei.com ([169.254.5.62]) with mapi id 14.03.0301.000; Mon, 14 Aug 2017 09:47:35 -0700 From: Haoyu song To: yanshen , "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgAbomeg Date: Mon, 14 Aug 2017 16:47:34 +0000 Message-ID: <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.213.49.156] Content-Type: multipart/alternative; boundary="_000_78A2745BE9B57D4F9D27F86655EB87F92599490ESJCEML701CHMchi_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020201.5991D431.0107, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.3.191, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 6e8070943f36dfba4ff02f5725b4f6b6 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 14 Aug 2017 16:47:51 -0000 --_000_78A2745BE9B57D4F9D27F86655EB87F92599490ESJCEML701CHMchi_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Yansen, I see two key use cases are missing in the current list: root cause analysi= s and anomaly detection. Those two are likely to use ML-based solutions and= the first one has already received a lot of research. Haoyu From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M; mskim16@etri.re.kr Track: [Jerome] https://www.ietf.org/mail-archive/web/id= net/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive= /web/idnet/current/msg00153.html Abstract: [Jerome] collect flow-level traffic metrics suc= h as protocol information but also meta metrics such as distribution of pac= ket sizes, inter-arrival times... Then use such information to label the tr= affic with the underlying application assuming that the granularity of clas= sification may vary (type of application, exact application name, version..= .) [Min-Suk Kim] continuously collect packet data,= then applying learning process for traffic classification with generating = application using deep learning models such as CNN (convolutional neural ne= twork) and RNN (recurrent neural network). Data-set to apply into the model= s are generated by processing with features of information from flow in pac= ket data. 2.6 TBD 3. Data Focus 3.1 Data attribute 3.2 Data format 3.3 TBD 4. Support Technologies 4.1 Benchmarking Framework Proposed by: pedro@nict.go.jp Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00146.html Abstract: A proper benchmarking framework comprises a se= t of reference procedures, methods, and models that can (or better *must*) = be followed to assess the quality of an AI mechanism proposed to be applied= to the network management/control area. Moreover, and much more specific t= o the IDNET topics, is the inclusion, dependency, or just the general relat= ion of a standard format enforced to the data that is used (input) and prod= uced (output) by the framework, so a kind of "data market" can arise withou= t requiring to transform the data. The initial scope of input/output data w= ould be the datasets, but also the new knowledge items that are stated as a= result of applying the benchmarking procedures defined by the framework, w= hich can be collected together to build a database of benchmark results, or= just contrasted with other existing entries in the database to know the po= sition of the solution just evaluated. This increases the usefulness of IDN= ET. 4.2 TBD --_000_78A2745BE9B57D4F9D27F86655EB87F92599490ESJCEML701CHMchi_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Yanse= n,

=  

I see= two key use cases are missing in the current list: root cause analysis and= anomaly detection. Those two are likely to use ML-based solutions and the = first one has already received a lot of research.

=  

Haoyu=

 

From: = IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

 =

Dear all,

 

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

 

If I miss something important, please let me know. A= pologized in advance.

 

Yansen

 

 

---------  Roadmap  ---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

---------  Roadmap End  ---------

 

 

1. Gap and Requirement Analysis

    1.1 Network Management requiremen= t

    1.2 TBD

2. Use Cases

    2.1 Traffic Prediction=

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Collect = the history traffic data and external data which may influence the traffic.= Predict the traffic in short/long/specific term. Avoid the congestion or r= isk in previously.

        &nbs= p;         

    2.2 QoS Management

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Use mult= iple paths to distribute the traffic flows. Adjust the percentages. Avoid c= ongestion and ensure QoS.

        

    2.3 Application (and/or DDoS= ) detection

        &nbs= p;          Proposed by: aydinulas@gmx.net

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html<= /o:p>

        &nbs= p;          Abstract: Detect t= he application (or attack) from network packets (HTTPS or plain) Collect th= e history traffic data and identify a service or attack (ex: Skype, Viber, = DDoS attack etc.)

        

         2.4= QoE Management

        &nbs= p;          Proposed by: albert.cabellos@gmail.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html<= /o:p>

        &nbs= p;          Abstract: Collect = low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then= use ML to understand what is the relation between satisfactory QoE and the= low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

        

         2.5= (Encrypted) Traffic Classification

        &nbs= p;          Proposed by: jerome.francois@inria.fr; mskim16= @etri.re.kr

        &nbs= p;          Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Mi= n-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html

        &nbs= p;          Abstract:

        &nbs= p;            &= nbsp;      [Jerome] collect flow-level traffic met= rics such as protocol information but also meta metrics such as distributio= n of packet sizes, inter-arrival times... Then use such information to labe= l the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)

        &nbs= p;            &= nbsp;      [Min-Suk Kim] continuously collect pack= et data, then applying learning process for traffic classification with gen= erating application using deep learning models such as CNN (convolutional n= eural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= o:p>

        &nbs= p;         

         2.6= TBD

        

3. Data Focus

    3.1 Data attribute

    3.2 Data format

    3.3 TBD

        

4. Support Technologies

    4.1 Benchmarking Framework

        &nbs= p;          Proposed by: pedro@nict.go.jp

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html<= /o:p>

        &nbs= p;          Abstract: A proper= benchmarking framework comprises a set of reference procedures, methods, a= nd models that can (or better *must*) be followed to assess the quality of = an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.=

        

    4.2 TBD

--_000_78A2745BE9B57D4F9D27F86655EB87F92599490ESJCEML701CHMchi_-- From nobody Mon Aug 14 10:24:22 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id CABB113226B for ; Mon, 14 Aug 2017 10:24:20 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2 X-Spam-Level: X-Spam-Status: No, score=-2 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (1024-bit key) header.d=unicauca.edu.co Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 3cwJjeRNwG81 for ; Mon, 14 Aug 2017 10:24:17 -0700 (PDT) Received: from mail-ua0-x236.google.com (mail-ua0-x236.google.com [IPv6:2607:f8b0:400c:c08::236]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 690991321B9 for ; Mon, 14 Aug 2017 10:24:17 -0700 (PDT) Received: by mail-ua0-x236.google.com with SMTP id f9so39154262uaf.4 for ; Mon, 14 Aug 2017 10:24:17 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=unicauca.edu.co; s=google; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=KZ9Advpc0L0+Mh11exjvqmn/5jnDWewFMzBvLz0KlPo=; b=DaoJUHeGhFgSrV2hJgXl4Masy5afUI11NGef+NmpaK3iiSnE+v9JNjqR7pEEepnDxn Rj/m/ns45lgckbMItFY7kkOTI7e+D1akxn7JWhuvCmMgvBKEoxUGAPZcLWbKKgigN0Ls IFZ9/AVD55OkAm257b6qcVYuJiaQcqdZ9fniQ= X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=KZ9Advpc0L0+Mh11exjvqmn/5jnDWewFMzBvLz0KlPo=; b=Uw1nT4TwF5ac+5QdoqS++w3yjY0DuSeFNAS/3b/d5icZ7Q/S61ozHnbqNT89SZFgo3 3EEUUxweRN3nbx/zaI8+6yb3uAmAw0HnaH4u/8Fzr+RXZyDble9tDlaWJRXxuqa6V2gf p2H4jTiypBWFV/FjlH4HlfZJo0+oUgcFENz/61gJUEIZ/IJetJECEeuT2JmazkLhL4Cx 5YQtArTsaybK8FnNoJCqQEgor9nQtkdrYXktmjuajSg9F0tss9SBPQcP007vZAhwDSL8 223TjMAg0ox351eL4BRCXVctunW1LtzA6Ux/4PUGTSYEQ+ffy/lmsSd2BVwLzh2d9sog ygJA== X-Gm-Message-State: AHYfb5hNkltcwE6Qa4SHZeAacZ1Hg+saSFrcuhOU3Syd0c0z9ngirZ5k 4Q3i8+q9ClyP7wz0+p2J6kFhdK6rQu6dP+/tDTMiQT7+XyG7i7TkYAZY0BkWgiPDXm8Qu1fUjBq IdALPHBGF X-Received: by 10.176.78.219 with SMTP id x27mr15157618uah.144.1502731456397; Mon, 14 Aug 2017 10:24:16 -0700 (PDT) MIME-Version: 1.0 Received: by 10.103.46.216 with HTTP; Mon, 14 Aug 2017 10:23:56 -0700 (PDT) In-Reply-To: <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> From: Oscar Mauricio Caicedo Rendon Date: Mon, 14 Aug 2017 12:23:56 -0500 Message-ID: To: Haoyu song Cc: yanshen , "idnet@ietf.org" Content-Type: multipart/alternative; boundary="f403045ea5cea72d2c0556b9ee49" Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 14 Aug 2017 17:24:21 -0000 --f403045ea5cea72d2c0556b9ee49 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Dear all, Scaling mechanisms in NFV can leverage ML features. For instance, Reinforcement Learning could be used to tune policies of VNFs scaling (in memory, processing, number of instances, and so on), targeted to improve VNFs performance. Oscar On Mon, Aug 14, 2017 at 11:47 AM, Haoyu song wrote: > Yansen, > > > > I see two key use cases are missing in the current list: root cause > analysis and anomaly detection. Those two are likely to use ML-based > solutions and the first one has already received a lot of research. > > > > Haoyu > > > > *From:* IDNET [mailto:idnet-bounces@ietf.org] *On Behalf Of *yanshen > *Sent:* Sunday, August 13, 2017 8:36 PM > *To:* idnet@ietf.org > *Subject:* [Idnet] Summary 20170814 & IDN dedicated session call for case > > > > Dear all, > > > > Here is a summary and some index (2017.08.14). Till now, whatever the cas= e > is supported or not, I tried to organize all the content and keep the cor= e > part. It is still welcome to contribute and discuss. > > > > If I miss something important, please let me know. Apologized in advance. > > > > Yansen > > > > > > --------- Roadmap --------- > > ***Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > > Sep. : Refining the cases and abstract the common elements > > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > > Nov.: F2F discussions on IETF100 > > --------- Roadmap End --------- > > > > > > 1. Gap and Requirement Analysis > > 1.1 Network Management requirement > > 1.2 TBD > > 2. Use Cases > > 2.1 Traffic Prediction > > Proposed by: yanshen@huawei.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00131.html > > Abstract: Collect the history traffic data and externa= l > data which may influence the traffic. Predict the traffic in > short/long/specific term. Avoid the congestion or risk in previously. > > > > 2.2 QoS Management > > Proposed by: yanshen@huawei.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00131.html > > Abstract: Use multiple paths to distribute the traffic > flows. Adjust the percentages. Avoid congestion and ensure QoS. > > > > 2.3 Application (and/or DDoS) detection > > Proposed by: aydinulas@gmx.net > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00133.html > > Abstract: Detect the application (or attack) from > network packets (HTTPS or plain) Collect the history traffic data and > identify a service or attack (ex: Skype, Viber, DDoS attack etc.) > > > > 2.4 QoE Management > > Proposed by: albert.cabellos@gmail.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00137.html > > Abstract: Collect low-level metrics (SNR, latency, > jitter, losses, etc) and measure QoE. Then use ML to understand what is t= he > relation between satisfactory QoE and the low-level metrics. As an exampl= e > learn that when delay>N then QoE is degraded, but when M is satisfactory for the customers (please note that QoE cannot be measure= d > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > > > > 2.5 (Encrypted) Traffic Classification > > Proposed by: jerome.francois@inria.fr; > mskim16@etri.re.kr > > Track: [Jerome] https://www.ietf.org/mail- > archive/web/idnet/current/msg00141.html ; [Min-Suk Kim] > https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html > > Abstract: > > [Jerome] collect flow-level traffic metrics > such as protocol information but also meta metrics such as distribution o= f > packet sizes, inter-arrival times... Then use such information to label t= he > traffic with the underlying application assuming that the granularity of > classification may vary (type of application, exact application name, > version...) > > [Min-Suk Kim] continuously collect packet > data, then applying learning process for traffic classification with > generating application using deep learning models such as CNN > (convolutional neural network) and RNN (recurrent neural network). Data-s= et > to apply into the models are generated by processing with features of > information from flow in packet data. > > > > 2.6 TBD > > > > 3. Data Focus > > 3.1 Data attribute > > 3.2 Data format > > 3.3 TBD > > > > 4. Support Technologies > > 4.1 Benchmarking Framework > > Proposed by: pedro@nict.go.jp > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00146.html > > Abstract: A proper benchmarking framework comprises a > set of reference procedures, methods, and models that can (or better > *must*) be followed to assess the quality of an AI mechanism proposed to = be > applied to the network management/control area. Moreover, and much more > specific to the IDNET topics, is the inclusion, dependency, or just the > general relation of a standard format enforced to the data that is used > (input) and produced (output) by the framework, so a kind of "data market= " > can arise without requiring to transform the data. The initial scope of > input/output data would be the datasets, but also the new knowledge items > that are stated as a result of applying the benchmarking procedures defin= ed > by the framework, which can be collected together to build a database of > benchmark results, or just contrasted with other existing entries in the > database to know the position of the solution just evaluated. This > increases the usefulness of IDNET. > > > > 4.2 TBD > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --=20 *Oscar Mauricio Caicedo Rend=C3=B3n* *PhD Computer Science - Federal University of Rio Grande do Sul* *Full Profesor - University of Cauca* --=20 ------------------------------ *Hacia una Universidad comprometida con la Paz Territorial* --f403045ea5cea72d2c0556b9ee49 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Dear all,

Scaling mechanisms in NFV can leverage ML= features. For instance, Reinforcement Learning could be used to tune polic= ies of VNFs scaling (in memory, processing, number of instances, and so on)= , targeted to improve VNFs performance.

Oscar

On Mon, Aug 14, 2017 at 11:47 = AM, Haoyu song <haoyu.song@huawei.com> wrote:

Yanse= n,

=C2=A0

I see= two key use cases are missing in the current list: root cause analysis and= anomaly detection. Those two are likely to use ML-based solutions and the = first one has already received a lot of research.

=C2=A0

Haoyu=

=C2=A0

From: = IDNET [mailto:i= dnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.o= rg
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

=C2= =A0

Dear all,

=C2=A0

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

=C2=A0

If I miss something important, please let me know. A= pologized in advance.

=C2=A0

Yansen

=C2=A0

=C2=A0

--------- =C2=A0Roadmap =C2=A0---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

--------- =C2=A0Roadmap End =C2=A0---------

=C2=A0

=C2=A0

1. Gap and Requirement Analysis

=C2=A0=C2=A0=C2=A0 1.1 Network Management requiremen= t

=C2=A0=C2=A0=C2=A0 1.2 TBD

2. Use Cases

=C2=A0=C2=A0=C2=A0 2.1 Traffic Prediction<= /u>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: yanshen@huawei.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Collect= the history traffic data and external data which may influence the traffic= . Predict the traffic in short/long/specific term. Avoid the congestion or = risk in previously.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0 2.2 QoS Management<= /p>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: yanshen@huawei.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Use mul= tiple paths to distribute the traffic flows. Adjust the percentages. Avoid = congestion and ensure QoS.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0=C2=A02.3 Application (and/or DDoS= ) detection

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: aydinulas@gmx.net

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Detect = the application (or attack) from network packets (HTTPS or plain) Collect t= he history traffic data and identify a service or attack (ex: Skype, Viber,= DDoS attack etc.)

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 2.4= QoE Management

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: albert.cabellos@gmail.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Collect= low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. The= n use ML to understand what is the relation between satisfactory QoE and th= e low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 2.5= (Encrypted) Traffic Classification

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: jerome.francois@inria.fr; mskim16@etri.re.kr

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: [Jerome] <= a href=3D"https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html= " target=3D"_blank"> https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html= ; [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00153.html

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: =

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 [Jerome] collect flow-level traffic me= trics such as protocol information but also meta metrics such as distributi= on of packet sizes, inter-arrival times... Then use such information to lab= el the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)<= u>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 [Min-Suk Kim] continuously collect pac= ket data, then applying learning process for traffic classification with ge= nerating application using deep learning models such as CNN (convolutional = neural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= u>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 2.6= TBD

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

3. Data Focus

=C2=A0=C2=A0=C2=A0 3.1 Data attribute<= /p>

=C2=A0=C2=A0=C2=A0 3.2 Data format

=C2=A0=C2=A0=C2=A0 3.3 TBD

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

4. Support Technologies

=C2=A0=C2=A0=C2=A0 4.1 Benchmarking Framework=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: pedro@nict.go.jp

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: A prope= r benchmarking framework comprises a set of reference procedures, methods, = and models that can (or better *must*) be followed to assess the quality of= an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0 4.2 TBD


_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet




--
Oscar Ma= uricio Caicedo Rend=C3=B3n
PhD Computer Science -=C2=A0Federal University of Rio Grande do Sul=
Full Profesor - University of Cauca

= Hacia una Universidad comprometida con la Paz Territorial

 --f403045ea5cea72d2c0556b9ee49-- From nobody Mon Aug 14 10:25:12 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 69D451323AF for ; Mon, 14 Aug 2017 10:25:11 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2 X-Spam-Level: X-Spam-Status: No, score=-2 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (1024-bit key) header.d=unicauca.edu.co Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id JheA1skbei8X for ; Mon, 14 Aug 2017 10:25:08 -0700 (PDT) Received: from mail-ua0-x22c.google.com (mail-ua0-x22c.google.com [IPv6:2607:f8b0:400c:c08::22c]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 4524E13226B for ; Mon, 14 Aug 2017 10:25:08 -0700 (PDT) Received: by mail-ua0-x22c.google.com with SMTP id d29so39183971uai.2 for ; Mon, 14 Aug 2017 10:25:08 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=unicauca.edu.co; s=google; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=mDWXplNtAvI1SCexKMCyRs3ukuscJi3Kkh3RYHYKiXE=; b=NQepQItIqdK75gccpSVrpjLuajqj1CQ3UU4COr/IA3/S/gWhF8r0+GJMmeaBG/Vq1Y 4Qm5I9kC1SjI9g2zOTD2x8F/9oBILpcdcyyo+Bs8r2gxNFk21XEFUYJeiHpfEU8FUEpF /cehjmhK+80JkuvtcdXhODLHtAJ3p9Cl92p1o= X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=mDWXplNtAvI1SCexKMCyRs3ukuscJi3Kkh3RYHYKiXE=; b=XUP2kkaoKe4uEn/xpho9dx08ILp5p11hY3JGaZWQRKIpBbLYZFeMXaG8g0GKG4MM3A 6ZE6x+GAcP5yR1jH32xfEi/yoKchBlofPvYJAFZbPbFRW4Kj5TFwv0JjbsyCaIE9Bg+6 LsP3WU3W4cb95kq/rWdI+fYvr0TqakDMxhDtfMNjONHjcbBZYHudWUSt41jLebdF1HWG gKwVYC5SeLBPCrSZTNWiGpxTTSO2YS0R1BFSWqQvD4ESwRASiK4HtEA7aTPHjGrGBFJ/ kR23dy20kMGovQ7U1IYtDeHSlWn/p4f3d7uflN5qpdQ0R4Tl290QeRaCB9sj6XLoKUIM jMVg== X-Gm-Message-State: AHYfb5horZ3AOsWGh0FVctWmLHLsqiDN06+uFhfdlsBPP8/pob1glewt JMfM/Nq3UAJSz5LU0omyCyl+7G5gOZvjd9scM+Ck20yMxlF1vtyKD5FyNa8833K5DSRjQYL4ZNg wo0hdYQ== X-Received: by 10.176.80.193 with SMTP id d1mr17539136uaa.124.1502731507331; Mon, 14 Aug 2017 10:25:07 -0700 (PDT) MIME-Version: 1.0 Received: by 10.103.46.216 with HTTP; Mon, 14 Aug 2017 10:24:46 -0700 (PDT) In-Reply-To: <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> From: Oscar Mauricio Caicedo Rendon Date: Mon, 14 Aug 2017 12:24:46 -0500 Message-ID: To: Haoyu song Cc: yanshen , "idnet@ietf.org" Content-Type: multipart/alternative; boundary="94eb2c19211eb05fdd0556b9f18b" Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 14 Aug 2017 17:25:11 -0000 --94eb2c19211eb05fdd0556b9f18b Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Dear all, Scaling mechanisms in NFV can leverage ML features. In particular, for instance, Reinforcement Learning could be used to tune policies scaling target to On Mon, Aug 14, 2017 at 11:47 AM, Haoyu song wrote: > Yansen, > > > > I see two key use cases are missing in the current list: root cause > analysis and anomaly detection. Those two are likely to use ML-based > solutions and the first one has already received a lot of research. > > > > Haoyu > > > > *From:* IDNET [mailto:idnet-bounces@ietf.org] *On Behalf Of *yanshen > *Sent:* Sunday, August 13, 2017 8:36 PM > *To:* idnet@ietf.org > *Subject:* [Idnet] Summary 20170814 & IDN dedicated session call for case > > > > Dear all, > > > > Here is a summary and some index (2017.08.14). Till now, whatever the cas= e > is supported or not, I tried to organize all the content and keep the cor= e > part. It is still welcome to contribute and discuss. > > > > If I miss something important, please let me know. Apologized in advance. > > > > Yansen > > > > > > --------- Roadmap --------- > > ***Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > > Sep. : Refining the cases and abstract the common elements > > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > > Nov.: F2F discussions on IETF100 > > --------- Roadmap End --------- > > > > > > 1. Gap and Requirement Analysis > > 1.1 Network Management requirement > > 1.2 TBD > > 2. Use Cases > > 2.1 Traffic Prediction > > Proposed by: yanshen@huawei.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00131.html > > Abstract: Collect the history traffic data and externa= l > data which may influence the traffic. Predict the traffic in > short/long/specific term. Avoid the congestion or risk in previously. > > > > 2.2 QoS Management > > Proposed by: yanshen@huawei.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00131.html > > Abstract: Use multiple paths to distribute the traffic > flows. Adjust the percentages. Avoid congestion and ensure QoS. > > > > 2.3 Application (and/or DDoS) detection > > Proposed by: aydinulas@gmx.net > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00133.html > > Abstract: Detect the application (or attack) from > network packets (HTTPS or plain) Collect the history traffic data and > identify a service or attack (ex: Skype, Viber, DDoS attack etc.) > > > > 2.4 QoE Management > > Proposed by: albert.cabellos@gmail.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00137.html > > Abstract: Collect low-level metrics (SNR, latency, > jitter, losses, etc) and measure QoE. Then use ML to understand what is t= he > relation between satisfactory QoE and the low-level metrics. As an exampl= e > learn that when delay>N then QoE is degraded, but when M is satisfactory for the customers (please note that QoE cannot be measure= d > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > > > > 2.5 (Encrypted) Traffic Classification > > Proposed by: jerome.francois@inria.fr; > mskim16@etri.re.kr > > Track: [Jerome] https://www.ietf.org/mail- > archive/web/idnet/current/msg00141.html ; [Min-Suk Kim] > https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html > > Abstract: > > [Jerome] collect flow-level traffic metrics > such as protocol information but also meta metrics such as distribution o= f > packet sizes, inter-arrival times... Then use such information to label t= he > traffic with the underlying application assuming that the granularity of > classification may vary (type of application, exact application name, > version...) > > [Min-Suk Kim] continuously collect packet > data, then applying learning process for traffic classification with > generating application using deep learning models such as CNN > (convolutional neural network) and RNN (recurrent neural network). Data-s= et > to apply into the models are generated by processing with features of > information from flow in packet data. > > > > 2.6 TBD > > > > 3. Data Focus > > 3.1 Data attribute > > 3.2 Data format > > 3.3 TBD > > > > 4. Support Technologies > > 4.1 Benchmarking Framework > > Proposed by: pedro@nict.go.jp > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00146.html > > Abstract: A proper benchmarking framework comprises a > set of reference procedures, methods, and models that can (or better > *must*) be followed to assess the quality of an AI mechanism proposed to = be > applied to the network management/control area. Moreover, and much more > specific to the IDNET topics, is the inclusion, dependency, or just the > general relation of a standard format enforced to the data that is used > (input) and produced (output) by the framework, so a kind of "data market= " > can arise without requiring to transform the data. The initial scope of > input/output data would be the datasets, but also the new knowledge items > that are stated as a result of applying the benchmarking procedures defin= ed > by the framework, which can be collected together to build a database of > benchmark results, or just contrasted with other existing entries in the > database to know the position of the solution just evaluated. This > increases the usefulness of IDNET. > > > > 4.2 TBD > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --=20 *Oscar Mauricio Caicedo Rend=C3=B3n* *PhD Computer Science - Federal University of Rio Grande do Sul* *Full Profesor - University of Cauca* --=20 ------------------------------ *Hacia una Universidad comprometida con la Paz Territorial* --94eb2c19211eb05fdd0556b9f18b Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Dear all,

Scaling mechanisms in NFV can = leverage ML features. In particular, for instance, Reinforcement Learning c= ould be used to tune policies scaling target to

On Mon, Aug 14, 2017 at 11:47 AM, = Haoyu song <haoyu.song@huawei.com> wrote:

Yanse= n,

=C2=A0

I see= two key use cases are missing in the current list: root cause analysis and= anomaly detection. Those two are likely to use ML-based solutions and the = first one has already received a lot of research.

=C2=A0

Haoyu=

=C2=A0=

From: = IDNET [mailto:i= dnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.o= rg
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

=C2= =A0

Dear all,

=C2=A0

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

=C2=A0

If I miss something important, please let me know. A= pologized in advance.

=C2=A0

Yansen

=C2=A0

=C2=A0

--------- =C2=A0Roadmap =C2=A0---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

--------- =C2=A0Roadmap End =C2=A0---------

=C2=A0

=C2=A0

1. Gap and Requirement Analysis

=C2=A0=C2=A0=C2=A0 1.1 Network Management requiremen= t

=C2=A0=C2=A0=C2=A0 1.2 TBD

2. Use Cases

=C2=A0=C2=A0=C2=A0 2.1 Traffic Prediction<= /u>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: yanshen@huawei.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Collect= the history traffic data and external data which may influence the traffic= . Predict the traffic in short/long/specific term. Avoid the congestion or = risk in previously.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0 2.2 QoS Management<= /p>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: yanshen@huawei.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Use mul= tiple paths to distribute the traffic flows. Adjust the percentages. Avoid = congestion and ensure QoS.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0=C2=A02.3 Application (and/or DDoS= ) detection

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: aydinulas@gmx.net

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Detect = the application (or attack) from network packets (HTTPS or plain) Collect t= he history traffic data and identify a service or attack (ex: Skype, Viber,= DDoS attack etc.)

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 2.4= QoE Management

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: albert.cabellos@gmail.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: Collect= low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. The= n use ML to understand what is the relation between satisfactory QoE and th= e low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 2.5= (Encrypted) Traffic Classification

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: jerome.francois@inria.fr; mskim16@etri.re.kr

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: [Jerome] <= a href=3D"https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html= " target=3D"_blank"> https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html= ; [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00153.html

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: =

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 [Jerome] collect flow-level traffic me= trics such as protocol information but also meta metrics such as distributi= on of packet sizes, inter-arrival times... Then use such information to lab= el the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)<= u>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 [Min-Suk Kim] continuously collect pac= ket data, then applying learning process for traffic classification with ge= nerating application using deep learning models such as CNN (convolutional = neural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= u>

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 2.6= TBD

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

3. Data Focus

=C2=A0=C2=A0=C2=A0 3.1 Data attribute<= /p>

=C2=A0=C2=A0=C2=A0 3.2 Data format

=C2=A0=C2=A0=C2=A0 3.3 TBD

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

4. Support Technologies

=C2=A0=C2=A0=C2=A0 4.1 Benchmarking Framework=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Proposed by: pedro@nict.go.jp

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Abstract: A prope= r benchmarking framework comprises a set of reference procedures, methods, = and models that can (or better *must*) be followed to assess the quality of= an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 =

=C2=A0=C2=A0=C2=A0 4.2 TBD


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--
Oscar Ma= uricio Caicedo Rend=C3=B3n
PhD Computer Science -=C2=A0Federal University of Rio Grande do Sul=
Full Profesor - University of Cauca

= Hacia una Universidad comprometida con la Paz Territorial

 --94eb2c19211eb05fdd0556b9f18b-- From nobody Mon Aug 14 11:09:44 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 385321323C4 for ; Mon, 14 Aug 2017 11:09:42 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.4 X-Spam-Level: X-Spam-Status: No, score=-2.4 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, FREEMAIL_FORGED_FROMDOMAIN=0.199, FREEMAIL_FROM=0.001, HEADER_FROM_DIFFERENT_DOMAINS=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id t9T3PyMRRw3V for ; Mon, 14 Aug 2017 11:09:40 -0700 (PDT) Received: from mail-yw0-x22d.google.com (mail-yw0-x22d.google.com [IPv6:2607:f8b0:4002:c05::22d]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D44081323C0 for ; Mon, 14 Aug 2017 11:09:39 -0700 (PDT) Received: by mail-yw0-x22d.google.com with SMTP id l82so59340486ywc.2 for ; Mon, 14 Aug 2017 11:09:39 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:sender:in-reply-to:references:from:date:message-id :subject:to:cc; bh=naoam6jBeSCkukaDYYMiHXLH45eeMe+JrIjHyVXSSBc=; b=JUzLriZsj3N/VfBeRvPj7QJrLdZkkyD/4F4KaVSEoYlYSOdDrd3gkljgSq2U6if+qn sM5AXVFiQW4c5yqHco3Ih/J8cgWiOGRaS9Aefp0TMlrIsszrzEreY+1+yWHb5soZLyGD HnAOJs3MCFYYaiLnWhlPMvMq6/MBZotqmZ6zTo2QKTW56QH351nuDKcZej8yjKY5Qb4o H3v/oPxv13y7jzp7L1Xq07mhLqFAIwJ6jYXJW9hXuKOlie8wLr5Ved9d7/COuQkcAo74 RYdax77SJLMTwHcUjnWWWd4n3tOoadLl25qcpJbM5QnuNjnFWwENVeJvjdziLe3oGZVH d6XA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:sender:in-reply-to:references:from :date:message-id:subject:to:cc; bh=naoam6jBeSCkukaDYYMiHXLH45eeMe+JrIjHyVXSSBc=; b=Yj8o1r+KQ/CAvBB4iSncwh3S7pPBuZSdRnAsPBmpT0/958RXuZYeSfagSHRjp7RB3C FHE3jDY/WGPRSM97FiCSvGreqcch2ic+UHJ2LmjLrDoI+/vw+8HWp9kQxInv0YZnin7w ZxW8wHOY1bcVrVBwtAIQBNTtHSO1DTd80wJAyvglgVIU9qo/czGeFE2kxp3FLg/Jo8Og V42ahtIs0aKuAJUcdio2lvMLlI7g4aj94zvoQtStIP19Mc+c1AlcckO1tyhs3hN6VtAx +p3Jw1svLaqMnZna861mWgGToUFkD4dvzkqpsyVr1uAkz9pErWqv4ZtzIPPrrWJuypf1 3vUw== X-Gm-Message-State: AHYfb5hTK8CahDDWucaZdYqC/WhUT+H/uYEPTul6HHyTeHdH17pU+RR5 Ef/fjUYgR9FiON+fUTRbaAnJ9GkgLQ== X-Received: by 10.37.42.134 with SMTP id q128mr2012949ybq.100.1502734178984; Mon, 14 Aug 2017 11:09:38 -0700 (PDT) MIME-Version: 1.0 Sender: steniofernandes@gmail.com Received: by 10.37.104.74 with HTTP; Mon, 14 Aug 2017 11:08:58 -0700 (PDT) In-Reply-To: <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> From: Stenio Fernandes Date: Mon, 14 Aug 2017 14:08:58 -0400 X-Google-Sender-Auth: hpGP4BOQFL-Ms8TwNfn2TZc2sL0 Message-ID: To: Haoyu song Cc: yanshen , "idnet@ietf.org" Content-Type: text/plain; charset="UTF-8" Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 14 Aug 2017 18:09:42 -0000 Haoyu, I'm working on the anomaly detection use case and will send it to the list this week. Stenio On Mon, Aug 14, 2017 at 12:47 PM, Haoyu song wrote: > Yansen, > > > > I see two key use cases are missing in the current list: root cause analysis > and anomaly detection. Those two are likely to use ML-based solutions and > the first one has already received a lot of research. > > > > Haoyu > > > > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen > Sent: Sunday, August 13, 2017 8:36 PM > To: idnet@ietf.org > Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case > > > > Dear all, > > > > Here is a summary and some index (2017.08.14). Till now, whatever the case > is supported or not, I tried to organize all the content and keep the core > part. It is still welcome to contribute and discuss. > > > > If I miss something important, please let me know. Apologized in advance. > > > > Yansen > > > > > > --------- Roadmap --------- > > ***Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > > Sep. : Refining the cases and abstract the common elements > > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > > Nov.: F2F discussions on IETF100 > > --------- Roadmap End --------- > > > > > > 1. Gap and Requirement Analysis > > 1.1 Network Management requirement > > 1.2 TBD > > 2. Use Cases > > 2.1 Traffic Prediction > > Proposed by: yanshen@huawei.com > > Track: > https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html > > Abstract: Collect the history traffic data and external > data which may influence the traffic. Predict the traffic in > short/long/specific term. Avoid the congestion or risk in previously. > > > > 2.2 QoS Management > > Proposed by: yanshen@huawei.com > > Track: > https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html > > Abstract: Use multiple paths to distribute the traffic > flows. Adjust the percentages. Avoid congestion and ensure QoS. > > > > 2.3 Application (and/or DDoS) detection > > Proposed by: aydinulas@gmx.net > > Track: > https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html > > Abstract: Detect the application (or attack) from network > packets (HTTPS or plain) Collect the history traffic data and identify a > service or attack (ex: Skype, Viber, DDoS attack etc.) > > > > 2.4 QoE Management > > Proposed by: albert.cabellos@gmail.com > > Track: > https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html > > Abstract: Collect low-level metrics (SNR, latency, > jitter, losses, etc) and measure QoE. Then use ML to understand what is the > relation between satisfactory QoE and the low-level metrics. As an example > learn that when delay>N then QoE is degraded, but when M satisfactory for the customers (please note that QoE cannot be measured > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > > > > 2.5 (Encrypted) Traffic Classification > > Proposed by: jerome.francois@inria.fr; mskim16@etri.re.kr > > Track: [Jerome] > https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Min-Suk > Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html > > Abstract: > > [Jerome] collect flow-level traffic metrics such > as protocol information but also meta metrics such as distribution of packet > sizes, inter-arrival times... Then use such information to label the traffic > with the underlying application assuming that the granularity of > classification may vary (type of application, exact application name, > version...) > > [Min-Suk Kim] continuously collect packet data, > then applying learning process for traffic classification with generating > application using deep learning models such as CNN (convolutional neural > network) and RNN (recurrent neural network). Data-set to apply into the > models are generated by processing with features of information from flow in > packet data. > > > > 2.6 TBD > > > > 3. Data Focus > > 3.1 Data attribute > > 3.2 Data format > > 3.3 TBD > > > > 4. Support Technologies > > 4.1 Benchmarking Framework > > Proposed by: pedro@nict.go.jp > > Track: > https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html > > Abstract: A proper benchmarking framework comprises a set > of reference procedures, methods, and models that can (or better *must*) be > followed to assess the quality of an AI mechanism proposed to be applied to > the network management/control area. Moreover, and much more specific to the > IDNET topics, is the inclusion, dependency, or just the general relation of > a standard format enforced to the data that is used (input) and produced > (output) by the framework, so a kind of "data market" can arise without > requiring to transform the data. The initial scope of input/output data > would be the datasets, but also the new knowledge items that are stated as a > result of applying the benchmarking procedures defined by the framework, > which can be collected together to build a database of benchmark results, or > just contrasted with other existing entries in the database to know the > position of the solution just evaluated. This increases the usefulness of > IDNET. > > > > 4.2 TBD > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > -- Prof. Stenio Fernandes CIn/UFPE http://www.steniofernandes.com From nobody Mon Aug 14 19:38:13 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2FE811324A1 for ; Mon, 14 Aug 2017 19:38:11 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id GCuMNIuyxS4A for ; Mon, 14 Aug 2017 19:38:07 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D2F7F1324B0 for ; Mon, 14 Aug 2017 19:38:05 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml709-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTK95944; Tue, 15 Aug 2017 02:38:03 +0000 (GMT) Received: from DGGEMM404-HUB.china.huawei.com (10.3.20.212) by lhreml709-cah.china.huawei.com (10.201.108.32) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 15 Aug 2017 03:38:02 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.229]) by DGGEMM404-HUB.china.huawei.com ([10.3.20.212]) with mapi id 14.03.0301.000; Tue, 15 Aug 2017 10:37:51 +0800 From: yanshen To: Stenio Fernandes , Haoyu song CC: "idnet@ietf.org" Thread-Topic: [Idnet] Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgAbomeg//+R/gD//vUlAA== Date: Tue, 15 Aug 2017 02:37:51 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D298461D@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090202.59925E8C.0031, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.229, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 33e7d71d2678e87b375ff97fda67f005 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 15 Aug 2017 02:38:11 -0000 SGkgSGFveXUsDQoNCkFncmVlLiBUaGVzZSB0d28gY3J1Y2lhbCBjYXNlcyBpbiBOZXR3b3JrIE1h bmFnZW1lbnQgYXJlIHdoYXQgd2UgYXJlIGZvY3VzaW5nIG9uIG5vdy4gU2luY2Ugd2UgcGxhbiB0 byBvcmdhbml6ZSBhIGRlZGljYXRlZCBzZXNzaW9uIGluIE5NUkcsIGFsbCB0aGUgZGlzY3Vzc2lv biB3aWxsIGNvbnZlcmdlIHRvIHRoZSBhcmVhIG9mIE5ldHdvcmsgTWFuYWdlbWVudCBiZWZvcmUg Tm92LiANCg0KSnVzdCBleHBlY3QgU3RlbmlvJ3Mgb3V0cHV0IGZldyBkYXlzIGxhdGVyIDogKQ0K DQpZYW5zZW4NCg0KDQo+IC0tLS0tT3JpZ2luYWwgTWVzc2FnZS0tLS0tDQo+IEZyb206IHN0ZW5p b2Zlcm5hbmRlc0BnbWFpbC5jb20gW21haWx0bzpzdGVuaW9mZXJuYW5kZXNAZ21haWwuY29tXSBP bg0KPiBCZWhhbGYgT2YgU3RlbmlvIEZlcm5hbmRlcw0KPiBTZW50OiBUdWVzZGF5LCBBdWd1c3Qg MTUsIDIwMTcgMjowOSBBTQ0KPiBUbzogSGFveXUgc29uZyA8aGFveXUuc29uZ0BodWF3ZWkuY29t Pg0KPiBDYzogeWFuc2hlbiA8eWFuc2hlbkBodWF3ZWkuY29tPjsgaWRuZXRAaWV0Zi5vcmcNCj4g 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IA0KPiANCj4gDQo+IC0tDQo+IFByb2YuIFN0ZW5pbyBGZXJuYW5kZXMNCj4gQ0luL1VGUEUNCj4g aHR0cDovL3d3dy5zdGVuaW9mZXJuYW5kZXMuY29tDQo= From nobody Tue Aug 15 05:26:01 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E25AC120721 for ; Tue, 15 Aug 2017 05:25:58 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.219 X-Spam-Level: X-Spam-Status: No, score=-4.219 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id KZKxWRLq-JbI for ; Tue, 15 Aug 2017 05:25:54 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 57B6512008A for ; Tue, 15 Aug 2017 05:25:53 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml703-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTL90777; Tue, 15 Aug 2017 12:25:51 +0000 (GMT) Received: from DGGEMM405-HUB.china.huawei.com (10.3.20.213) by lhreml703-cah.china.huawei.com (10.201.108.44) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 15 Aug 2017 13:25:49 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.229]) by DGGEMM405-HUB.china.huawei.com ([10.3.20.213]) with mapi id 14.03.0301.000; Tue, 15 Aug 2017 20:25:07 +0800 From: yanshen To: Oscar Mauricio Caicedo Rendon CC: "idnet@ietf.org" Thread-Topic: [Idnet] Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgAbomeg//+FaQD//kAzIA== Date: Tue, 15 Aug 2017 12:25:06 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D2986923@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: multipart/alternative; boundary="_000_6AE399511121AB42A34ACEF7BF25B4D2986923DGGEMM505MBXchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090203.5992E84F.0041, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.229, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 33e7d71d2678e87b375ff97fda67f005 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 15 Aug 2017 12:25:59 -0000 --_000_6AE399511121AB42A34ACEF7BF25B4D2986923DGGEMM505MBXchina_ Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 SGkgT3NjYXIsDQoNClRoZXJlIGFyZSBxdWl0ZSBudW1iZXJzIG9mIHNjaGVtZXMgdGhhdCByZWxh dGVkIHdpdGggTkZWIHNjZW5hcmlvLg0KDQpQZXJzb25hbGx5LCB0aGlzIGNhc2UgY2FuIGJlIGNv bmNsdWRlZCBpbnRvIOKAnHJlc291cmNlIGFsbG9jYXRpb27igJ0uIE5vdCBvbmx5IHRoZSBWTkYg cmVzb3VyY2UsIGJ1dCBhbHNvIG90aGVyIHJlc291cmNlLiBTdWNoIGFzLA0KICAgICAgICAgKiBJ biBEYXRhIENlbnRlciBOZXR3b3JrLCB0aGUgbGluayByZXNvdXJjZSBvciBiYW5kd2lkdGggYWxs b2NhdGlvbi4NCiAgICAgICAgICogSW4gdGhlIGdhdGV3YXkgZGV2aWNlLCB0aGUgYmFuZHdpZHRo IGFsbG9jYXRpb24gb2YgZGlmZmVyZW50IGFwcGxpY2F0aW9ucyAvIHByaW9yaXRpZXMgLyBzdWJz Y3JpYmVycyAvIHBvcnRzIC8gZXRj4oCmDQogICAgICAgICAqIFRCRA0KDQpJIHRoaW5rIHRoYXQg dGhlIGJhc2ljIGlkZWFzIG9mIHRoZXNlIGNhc2VzIGFyZSBzaW1pbGFyOiByZWFsLXRpbWUgbWVh 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1kwmNz1PXMYq for ; Tue, 15 Aug 2017 05:49:04 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id DA4C9126BFD for ; Tue, 15 Aug 2017 05:49:02 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml704-cah.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DMR39443; Tue, 15 Aug 2017 12:49:00 +0000 (GMT) Received: from SJCEML702-CHM.china.huawei.com (10.208.112.38) by lhreml704-cah.china.huawei.com (10.201.108.45) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 15 Aug 2017 13:48:59 +0100 Received: from SJCEML701-CHM.china.huawei.com ([169.254.3.191]) by SJCEML702-CHM.china.huawei.com ([169.254.4.148]) with mapi id 14.03.0301.000; Tue, 15 Aug 2017 05:48:48 -0700 From: Hesham ElBakoury To: yanshen , "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgBFDo4A Date: Tue, 15 Aug 2017 12:48:47 +0000 Message-ID: References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.132.51] Content-Type: multipart/alternative; boundary="_000_C3855D43D6701846AD1151A536E7A05824700491SJCEML701CHMchi_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090201.5992EDBD.0052, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.3.191, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 43a6cfdcfe6e42dbffe3f653857679b4 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 15 Aug 2017 12:49:07 -0000 --_000_C3855D43D6701846AD1151A536E7A05824700491SJCEML701CHMchi_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Yanshen, In all of these use cases what problems we are trying to solve that have no= t been solved before or why existing solutions are not good enough ? Thanks Hesham From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M

Hi Ya= nshen,

=  

In al= l of these use cases what problems we are trying to solve that have not bee= n solved before or why existing solutions are not good enough ?<= /span>

=  

Thank= s

=  

Hesha= m

=  

From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

 =

Dear all,

 

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

 

If I miss something important, please let me know. A= pologized in advance.

 

Yansen

 

 

---------  Roadmap  ---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

---------  Roadmap End  ---------

 

 

1. Gap and Requirement Analysis

    1.1 Network Management requiremen= t

    1.2 TBD

2. Use Cases

    2.1 Traffic Prediction=

        &nbs= p;          Proposed by: yansh= en@huawei.com

        &nbs= p;          Track: https://www= .ietf.org/mail-archive/web/idnet/current/msg00131.html

        &nbs= p;          Abstract: Collect = the history traffic data and external data which may influence the traffic.= Predict the traffic in short/long/specific term. Avoid the congestion or r= isk in previously.

        &nbs= p;         

    2.2 QoS Management

        &nbs= p;          Proposed by: yansh= en@huawei.com

        &nbs= p;          Track: https://www= .ietf.org/mail-archive/web/idnet/current/msg00131.html

        &nbs= p;          Abstract: Use mult= iple paths to distribute the traffic flows. Adjust the percentages. Avoid c= ongestion and ensure QoS.

        

    2.3 Application (and/or DDoS= ) detection

        &nbs= p;          Proposed by: aydin= ulas@gmx.net

        &nbs= p;          Track: https://www= .ietf.org/mail-archive/web/idnet/current/msg00133.html

        &nbs= p;          Abstract: Detect t= he application (or attack) from network packets (HTTPS or plain) Collect th= e history traffic data and identify a service or attack (ex: Skype, Viber, = DDoS attack etc.)

        

         2.4= QoE Management

        &nbs= p;          Proposed by: alber= t.cabellos@gmail.com

        &nbs= p;          Track: https://www= .ietf.org/mail-archive/web/idnet/current/msg00137.html

        &nbs= p;          Abstract: Collect = low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then= use ML to understand what is the relation between satisfactory QoE and the= low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

        

         2.5= (Encrypted) Traffic Classification

        &nbs= p;          Proposed by: jerom= e.francois@inria.fr; mskim16@etri.re.kr

        &nbs= p;          Track: [Jerome] ht= tps://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Min-Suk = Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html=

        &nbs= p;          Abstract:

        &nbs= p;            &= nbsp;      [Jerome] collect flow-level traffic met= rics such as protocol information but also meta metrics such as distributio= n of packet sizes, inter-arrival times... Then use such information to labe= l the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)

        &nbs= p;            &= nbsp;      [Min-Suk Kim] continuously collect pack= et data, then applying learning process for traffic classification with gen= erating application using deep learning models such as CNN (convolutional n= eural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= o:p>

        &nbs= p;         

         2.6= TBD

        

3. Data Focus

    3.1 Data attribute

    3.2 Data format

    3.3 TBD

        

4. Support Technologies

    4.1 Benchmarking Framework

        &nbs= p;          Proposed by: pedro= @nict.go.jp

        &nbs= p;          Track: https://www= .ietf.org/mail-archive/web/idnet/current/msg00146.html

        &nbs= p;          Abstract: A proper= benchmarking framework comprises a set of reference procedures, methods, a= nd models that can (or better *must*) be followed to assess the quality of = an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.=

        

    4.2 TBD

--_000_C3855D43D6701846AD1151A536E7A05824700491SJCEML701CHMchi_-- From nobody Tue Aug 15 13:10:13 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 81B881321F6 for ; Tue, 15 Aug 2017 13:10:12 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.219 X-Spam-Level: X-Spam-Status: No, score=-4.219 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id ZUYCcuj6WEIF for ; Tue, 15 Aug 2017 13:10:09 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 9B0E313238E for ; Tue, 15 Aug 2017 13:10:08 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml701-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTM49325; Tue, 15 Aug 2017 20:10:05 +0000 (GMT) Received: from SJCEML703-CHM.china.huawei.com (10.208.112.39) by lhreml701-cah.china.huawei.com (10.201.108.42) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 15 Aug 2017 21:10:04 +0100 Received: from SJCEML701-CHM.china.huawei.com ([169.254.3.191]) by SJCEML703-CHM.china.huawei.com ([169.254.5.62]) with mapi id 14.03.0301.000; Tue, 15 Aug 2017 13:09:54 -0700 From: Haoyu song To: Hesham ElBakoury , yanshen , "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgBFDo4AAA5JSBA= Date: Tue, 15 Aug 2017 20:09:53 +0000 Message-ID: <78A2745BE9B57D4F9D27F86655EB87F925997F85@SJCEML701-CHM.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.213.49.223] Content-Type: multipart/alternative; boundary="_000_78A2745BE9B57D4F9D27F86655EB87F925997F85SJCEML701CHMchi_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090205.5993551E.0087, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.3.191, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 6e8070943f36dfba4ff02f5725b4f6b6 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 15 Aug 2017 20:10:13 -0000 --_000_78A2745BE9B57D4F9D27F86655EB87F925997F85SJCEML701CHMchi_ Content-Type: text/plain; charset="iso-2022-jp" Content-Transfer-Encoding: quoted-printable Obviously we are not going to apply ML on networking just for the sake of M= L. In my opinion, the only reason to apply ML is that it=1B$B!G=1B(Bs prove= d to be a better (or only) way to solve a problem. We have seen a few succe= ssful examples such as ETA and root cause analysis, and ML approaches seem = promising on solving some networking problems=1B$B!$=1B(B but I also agree = that we are still far from the conclusion (or don=1B$B!G=1B(Bt even have c= lear understanding) that all these use cases can be better solved by ML. Th= en it is even farther to think about standardizing the related protocols an= d interfaces at the current stage. That said, this doesn=1B$B!G=1B(Bt preve= nt us from studying the possibilities. The most important thing is to devel= op more solid use cases to demonstrate that ML is not only useful but prefe= rred. The current list is still too high level which doesn=1B$B!G=1B(Bt hel= p me gain such confidence yet. Hopefully more detailed reports will come. Haoyu From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Hesham ElBakoury Sent: Tuesday, August 15, 2017 5:49 AM To: yanshen ; idnet@ietf.org Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case Hi Yanshen, In all of these use cases what problems we are trying to solve that have no= t been solved before or why existing solutions are not good enough ? Thanks Hesham From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M; mskim16@etri.re.kr Track: [Jerome] https://www.ietf.org/mail-archive/web/id= net/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive= /web/idnet/current/msg00153.html Abstract: [Jerome] collect flow-level traffic metrics suc= h as protocol information but also meta metrics such as distribution of pac= ket sizes, inter-arrival times... Then use such information to label the tr= affic with the underlying application assuming that the granularity of clas= sification may vary (type of application, exact application name, version..= .) [Min-Suk Kim] continuously collect packet data,= then applying learning process for traffic classification with generating = application using deep learning models such as CNN (convolutional neural ne= twork) and RNN (recurrent neural network). Data-set to apply into the model= s are generated by processing with features of information from flow in pac= ket data. 2.6 TBD 3. Data Focus 3.1 Data attribute 3.2 Data format 3.3 TBD 4. Support Technologies 4.1 Benchmarking Framework Proposed by: pedro@nict.go.jp Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00146.html Abstract: A proper benchmarking framework comprises a se= t of reference procedures, methods, and models that can (or better *must*) = be followed to assess the quality of an AI mechanism proposed to be applied= to the network management/control area. Moreover, and much more specific t= o the IDNET topics, is the inclusion, dependency, or just the general relat= ion of a standard format enforced to the data that is used (input) and prod= uced (output) by the framework, so a kind of "data market" can arise withou= t requiring to transform the data. The initial scope of input/output data w= ould be the datasets, but also the new knowledge items that are stated as a= result of applying the benchmarking procedures defined by the framework, w= hich can be collected together to build a database of benchmark results, or= just contrasted with other existing entries in the database to know the po= sition of the solution just evaluated. This increases the usefulness of IDN= ET. 4.2 TBD --_000_78A2745BE9B57D4F9D27F86655EB87F925997F85SJCEML701CHMchi_ Content-Type: text/html; charset="iso-2022-jp" Content-Transfer-Encoding: quoted-printable

Obvio= usly we are not going to apply ML on networking just for the sake of ML. In= my opinion, the only reason to apply ML is that it=1B$B!G=1B(Bs proved to = be a better (or only) way to solve a problem. We have seen a few successful examples such as ETA and root cause analysis, a= nd ML approaches seem promising on solving some networking problems<= span lang=3D"ZH-CN" style=3D"font-size:11.0pt;font-family:SimSun;color:#1F4= 97D">=1B$B!$=1B(B but I also agree that= we are still far from  the conclusion (or don=1B$B!G=1B(Bt even have = clear understanding) that all these use cases can be better solved by ML. T= hen it is even farther to think about standardizing the related protocols and interfaces at the current stage. That said, this= doesn=1B$B!G=1B(Bt prevent us from studying the possibilities. The most im= portant thing is to develop more solid use cases to demonstrate that ML is = not only useful but preferred. The current list is still too high level which doesn=1B$B!G=1B(Bt help me gain such co= nfidence yet. Hopefully more detailed reports will come.<= /p>

=  

Haoyu=

 

From: = IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Hesham ElBakoury
Sent: Tuesday, August 15, 2017 5:49 AM
To: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session ca= ll for case

 =

Hi Ya= nshen,

=  

In al= l of these use cases what problems we are trying to solve that have not bee= n solved before or why existing solutions are not good enough ?<= /span>

=  

Thank= s

=  

Hesha= m

=  

From: IDNET [mailto:idnet-boun= ces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

 =

Dear all,

 

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

 

If I miss something important, please let me know. A= pologized in advance.

 

Yansen

 

 

---------  Roadmap  ---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

---------  Roadmap End  ---------

 

 

1. Gap and Requirement Analysis

    1.1 Network Management requiremen= t

    1.2 TBD

2. Use Cases

    2.1 Traffic Prediction=

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Collect = the history traffic data and external data which may influence the traffic.= Predict the traffic in short/long/specific term. Avoid the congestion or r= isk in previously.

        &nbs= p;         

    2.2 QoS Management

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Use mult= iple paths to distribute the traffic flows. Adjust the percentages. Avoid c= ongestion and ensure QoS.

        

    2.3 Application (and/or DDoS= ) detection

        &nbs= p;          Proposed by: aydinulas@gmx.net

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html<= /o:p>

        &nbs= p;          Abstract: Detect t= he application (or attack) from network packets (HTTPS or plain) Collect th= e history traffic data and identify a service or attack (ex: Skype, Viber, = DDoS attack etc.)

        

         2.4= QoE Management

        &nbs= p;          Proposed by: albert.cabellos@gmail.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html<= /o:p>

        &nbs= p;          Abstract: Collect = low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then= use ML to understand what is the relation between satisfactory QoE and the= low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

        

         2.5= (Encrypted) Traffic Classification

        &nbs= p;          Proposed by: jerome.francois@inria.fr; mskim16= @etri.re.kr

        &nbs= p;          Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Mi= n-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html

        &nbs= p;          Abstract:

        &nbs= p;            &= nbsp;      [Jerome] collect flow-level traffic met= rics such as protocol information but also meta metrics such as distributio= n of packet sizes, inter-arrival times... Then use such information to labe= l the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)

        &nbs= p;            &= nbsp;      [Min-Suk Kim] continuously collect pack= et data, then applying learning process for traffic classification with gen= erating application using deep learning models such as CNN (convolutional n= eural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= o:p>

        &nbs= p;         

         2.6= TBD

        

3. Data Focus

    3.1 Data attribute

    3.2 Data format

    3.3 TBD

        

4. Support Technologies

    4.1 Benchmarking Framework

        &nbs= p;          Proposed by: pedro@nict.go.jp

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html<= /o:p>

        &nbs= p;          Abstract: A proper= benchmarking framework comprises a set of reference procedures, methods, a= nd models that can (or better *must*) be followed to assess the quality of = an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.=

        

    4.2 TBD

--_000_78A2745BE9B57D4F9D27F86655EB87F925997F85SJCEML701CHMchi_-- From nobody Tue Aug 15 19:22:05 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 8B362132455 for ; Tue, 15 Aug 2017 19:22:03 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id dw8Szki9qYKL for ; Tue, 15 Aug 2017 19:22:00 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id E58481323C1 for ; Tue, 15 Aug 2017 19:21:59 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml704-cah.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DMS19913; Wed, 16 Aug 2017 02:21:57 +0000 (GMT) Received: from SJCEML702-CHM.china.huawei.com (10.208.112.38) by lhreml704-cah.china.huawei.com (10.201.108.45) with Microsoft SMTP Server (TLS) id 14.3.301.0; Wed, 16 Aug 2017 03:21:56 +0100 Received: from SJCEML701-CHM.china.huawei.com ([169.254.3.191]) by SJCEML702-CHM.china.huawei.com ([169.254.4.148]) with mapi id 14.03.0301.000; Tue, 15 Aug 2017 19:21:45 -0700 From: Hesham ElBakoury To: Haoyu song , yanshen , "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgBFDo4AAA5JSBAADruyIA== Date: Wed, 16 Aug 2017 02:21:44 +0000 Message-ID: References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F925997F85@SJCEML701-CHM.china.huawei.com> In-Reply-To: <78A2745BE9B57D4F9D27F86655EB87F925997F85@SJCEML701-CHM.china.huawei.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.135.45] Content-Type: multipart/alternative; boundary="_000_C3855D43D6701846AD1151A536E7A05824701BE3SJCEML701CHMchi_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090205.5993AC46.0022, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.3.191, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 43a6cfdcfe6e42dbffe3f653857679b4 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 16 Aug 2017 02:22:04 -0000 --_000_C3855D43D6701846AD1151A536E7A05824701BE3SJCEML701CHMchi_ Content-Type: text/plain; charset="iso-2022-jp" Content-Transfer-Encoding: quoted-printable I would think that the self-service revolution can provide useful use cases= for ML/AI. Hesham From: Haoyu song Sent: Tuesday, August 15, 2017 1:10 PM To: Hesham ElBakoury; yanshen; idnet@ietf.org Subject: RE: Summary 20170814 & IDN dedicated session call for case Obviously we are not going to apply ML on networking just for the sake of M= L. In my opinion, the only reason to apply ML is that it=1B$B!G=1B(Bs prove= d to be a better (or only) way to solve a problem. We have seen a few succe= ssful examples such as ETA and root cause analysis, and ML approaches seem = promising on solving some networking problems=1B$B!$=1B(B but I also agree = that we are still far from the conclusion (or don=1B$B!G=1B(Bt even have c= lear understanding) that all these use cases can be better solved by ML. Th= en it is even farther to think about standardizing the related protocols an= d interfaces at the current stage. That said, this doesn=1B$B!G=1B(Bt preve= nt us from studying the possibilities. The most important thing is to devel= op more solid use cases to demonstrate that ML is not only useful but prefe= rred. The current list is still too high level which doesn=1B$B!G=1B(Bt hel= p me gain such confidence yet. Hopefully more detailed reports will come. Haoyu From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Hesham ElBakoury Sent: Tuesday, August 15, 2017 5:49 AM To: yanshen ; idnet@ietf.org Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case Hi Yanshen, In all of these use cases what problems we are trying to solve that have no= t been solved before or why existing solutions are not good enough ? Thanks Hesham From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M; mskim16@etri.re.kr Track: [Jerome] https://www.ietf.org/mail-archive/web/id= net/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive= /web/idnet/current/msg00153.html Abstract: [Jerome] collect flow-level traffic metrics suc= h as protocol information but also meta metrics such as distribution of pac= ket sizes, inter-arrival times... Then use such information to label the tr= affic with the underlying application assuming that the granularity of clas= sification may vary (type of application, exact application name, version..= .) [Min-Suk Kim] continuously collect packet data,= then applying learning process for traffic classification with generating = application using deep learning models such as CNN (convolutional neural ne= twork) and RNN (recurrent neural network). Data-set to apply into the model= s are generated by processing with features of information from flow in pac= ket data. 2.6 TBD 3. Data Focus 3.1 Data attribute 3.2 Data format 3.3 TBD 4. Support Technologies 4.1 Benchmarking Framework Proposed by: pedro@nict.go.jp Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00146.html Abstract: A proper benchmarking framework comprises a se= t of reference procedures, methods, and models that can (or better *must*) = be followed to assess the quality of an AI mechanism proposed to be applied= to the network management/control area. Moreover, and much more specific t= o the IDNET topics, is the inclusion, dependency, or just the general relat= ion of a standard format enforced to the data that is used (input) and prod= uced (output) by the framework, so a kind of "data market" can arise withou= t requiring to transform the data. The initial scope of input/output data w= ould be the datasets, but also the new knowledge items that are stated as a= result of applying the benchmarking procedures defined by the framework, w= hich can be collected together to build a database of benchmark results, or= just contrasted with other existing entries in the database to know the po= sition of the solution just evaluated. This increases the usefulness of IDN= ET. 4.2 TBD --_000_C3855D43D6701846AD1151A536E7A05824701BE3SJCEML701CHMchi_ Content-Type: text/html; charset="iso-2022-jp" Content-Transfer-Encoding: quoted-printable

I wou= ld think that the self-service revolution can provide useful use cases for ML/AI.=

=  

Hesha= m

=  

From: Haoyu song
Sent: Tuesday, August 15, 2017 1:10 PM
To: Hesham ElBakoury; yanshen; idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 =

Obvio= usly we are not going to apply ML on networking just for the sake of ML. In= my opinion, the only reason to apply ML is that it=1B$B!G=1B(Bs proved to = be a better (or only) way to solve a problem. We have seen a few successful examples such as ETA and root cause analysis, a= nd ML approaches seem promising on solving some networking problems<= span lang=3D"ZH-CN" style=3D"font-size:11.0pt;font-family:SimSun; color:#1F497D">=1B$B!$=1B(B but I also agree that= we are still far from  the conclusion (or don=1B$B!G=1B(Bt even have = clear understanding) that all these use cases can be better solved by ML. T= hen it is even farther to think about standardizing the related protocols and interfaces at the current stage. That said, this= doesn=1B$B!G=1B(Bt prevent us from studying the possibilities. The most im= portant thing is to develop more solid use cases to demonstrate that ML is = not only useful but preferred. The current list is still too high level which doesn=1B$B!G=1B(Bt help me gain such co= nfidence yet. Hopefully more detailed reports will come.<= /p>

=  

Haoyu=

 

From: = IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Hesham ElBakoury
Sent: Tuesday, August 15, 2017 5:49 AM
To: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session ca= ll for case

 =

Hi Ya= nshen,

=  

In al= l of these use cases what problems we are trying to solve that have not bee= n solved before or why existing solutions are not good enough ?<= /span>

=  

Thank= s

=  

Hesha= m

=  

From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

 =

Dear all,

 

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

 

If I miss something important, please let me know. A= pologized in advance.

 

Yansen

 

 

---------  Roadmap  ---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

---------  Roadmap End  ---------

 

 

1. Gap and Requirement Analysis

    1.1 Network Management requiremen= t

    1.2 TBD

2. Use Cases

    2.1 Traffic Prediction=

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Collect = the history traffic data and external data which may influence the traffic.= Predict the traffic in short/long/specific term. Avoid the congestion or r= isk in previously.

        &nbs= p;         

    2.2 QoS Management

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Use mult= iple paths to distribute the traffic flows. Adjust the percentages. Avoid c= ongestion and ensure QoS.

        

    2.3 Application (and/or DDoS= ) detection

        &nbs= p;          Proposed by: aydinulas@gmx.net

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html<= /o:p>

        &nbs= p;          Abstract: Detect t= he application (or attack) from network packets (HTTPS or plain) Collect th= e history traffic data and identify a service or attack (ex: Skype, Viber, = DDoS attack etc.)

        

         2.4= QoE Management

        &nbs= p;          Proposed by: albert.cabellos@gmail.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html<= /o:p>

        &nbs= p;          Abstract: Collect = low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then= use ML to understand what is the relation between satisfactory QoE and the= low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

        

         2.5= (Encrypted) Traffic Classification

        &nbs= p;          Proposed by: jerome.francois@inria.fr; mskim16= @etri.re.kr

        &nbs= p;          Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Mi= n-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html

        &nbs= p;          Abstract:

        &nbs= p;            &= nbsp;      [Jerome] collect flow-level traffic met= rics such as protocol information but also meta metrics such as distributio= n of packet sizes, inter-arrival times... Then use such information to labe= l the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)

        &nbs= p;            &= nbsp;      [Min-Suk Kim] continuously collect pack= et data, then applying learning process for traffic classification with gen= erating application using deep learning models such as CNN (convolutional n= eural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= o:p>

        &nbs= p;         

         2.6= TBD

        

3. Data Focus

    3.1 Data attribute

    3.2 Data format

    3.3 TBD

        

4. Support Technologies

    4.1 Benchmarking Framework

        &nbs= p;          Proposed by: pedro@nict.go.jp

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html<= /o:p>

        &nbs= p;          Abstract: A proper= benchmarking framework comprises a set of reference procedures, methods, a= nd models that can (or better *must*) be followed to assess the quality of = an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.=

        

    4.2 TBD

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X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id nQryqgMb_hKa for ; Wed, 16 Aug 2017 07:26:35 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 3A2671321AE for ; Wed, 16 Aug 2017 07:26:34 -0700 (PDT) Received: from 172.18.7.190 (EHLO LHREML711-CAH.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTO01832; Wed, 16 Aug 2017 14:26:31 +0000 (GMT) Received: from DGGEMM405-HUB.china.huawei.com (10.3.20.213) by LHREML711-CAH.china.huawei.com (10.201.108.34) with Microsoft SMTP Server (TLS) id 14.3.301.0; Wed, 16 Aug 2017 15:26:27 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.229]) by DGGEMM405-HUB.china.huawei.com ([10.3.20.213]) with mapi id 14.03.0301.000; Wed, 16 Aug 2017 22:25:54 +0800 From: yanshen To: Hesham ElBakoury CC: "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgBFDo4AABo54cA= Date: Wed, 16 Aug 2017 14:25:53 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D298714C@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.109.110.25] Content-Type: multipart/alternative; boundary="_000_6AE399511121AB42A34ACEF7BF25B4D298714CDGGEMM505MBXchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020206.59945618.018F, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.229, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 6e8070943f36dfba4ff02f5725b4f6b6 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 16 Aug 2017 14:26:38 -0000 --_000_6AE399511121AB42A34ACEF7BF25B4D298714CDGGEMM505MBXchina_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Hesham, I think this question seems simple but really significant. Please forgive i= f the answer was redundant. And it is welcome to point out my lacks if you = have some different views. In general, the new method may bring potential benefits. There had been som= e discussions before in our mail list. Please search the keyword "benefit" = and check the result. But what I want to describe here are the following se= veral points. Firstly, in essence, the AI method can solve the scalability problem in eac= h use case but existing methods cannot. In other words, the existing method= s can hardly cover the complexity along with the huge increment of service = in the future by repeating the current solutions multi-times. For example,= if the cost (including time and economic) of configuring a VPN is C, so th= at it will cost at least 100*C for 100 VPNs (this is ideal, it is even wors= e in practice). Even though somebody may say the software programs can also= provide the automatic solution for this problems, but the over-linear incr= emental complexity will easily breakthrough the upper cost limit of that w= e can pay. However, since the AI method may solve the same question in yet = another new way so that it may change the complexity from over-linear growt= h to logarithmic growth (or even lower). This is significant because the cu= rrent methods and their improved version can hardly implement such a change= . I think this is one of the core problems that AI solves. It builds up a n= ew pattern that can bear the pressure from new services and new requirement= s, which the traditional method cannot support. Secondly, the new method can increase the automation level of the network b= ut not only provide an automatic program. The AI method can make more proce= ss automatic such as the decision and policy deploying. Some of them cannot= be implemented by program. For example, if we plan to capture and modify a= series of devices/networks configuration, how can we implement that? The d= evices may be from different venders. The topology may be different. Their = interfaces may be different. This situation is hard to solve by "if-else-th= en" logic or very hard. However, if we can abstract the configuration and t= he policies with some unified expression, such as using standard 0-1 series= or formatted packet, the consulting between various devices will be simpli= fied a lot. Of course, the "if-else-then" logic can implement that but the = core of problem is the complexity. However, some of the cases should not be mentioned. The aim of IDNet is int= roducing AI to serve the Network but not Application. Therefore, any case, = whose output implements only a new function or new application, may be not = suitable (personal view). Because it might be hardly produced by existing m= ethod but it is only a new tool but does not serve the network. We should f= ocus on the case, whose output produces a decision, a strategy, or a config= uration relative with network/device, or the cases that directly serve the = Network Management, especially improving the efficiency and decreasing the = cost. They are valuable. So the answer may not depend on whether the exist= ing methods can or cannot, but depends on the AI can or cannot provide a ne= w form. BTW, the new AI method may perform "better" than existing solutions. But in= my opinion, this "better" cannot become the sufficient condition to apply = because it is often covered by the cost of deployment. In a word, in any cases, the IDNet will provide a scalability solutions and= increase the automation level that the existing method and automatic softw= are programs cannot. And our work here is to explore the standardization po= ints and define the process, the data format, the architecture and etc., wh= ich support the compatibility for the various function entities. So that ma= ke the AI method enlarge the power of the network. Regards, Yansen From: Hesham ElBakoury Sent: Tuesday, August 15, 2017 8:49 PM To: yanshen ; idnet@ietf.org Subject: RE: Summary 20170814 & IDN dedicated session call for case Hi Yanshen, In all of these use cases what problems we are trying to solve that have no= t been solved before or why existing solutions are not good enough ? Thanks Hesham From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M; mskim16@etri.re.kr Track: [Jerome] https://www.ietf.org/mail-archive/web/id= net/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive= /web/idnet/current/msg00153.html Abstract: [Jerome] collect flow-level traffic metrics suc= h as protocol information but also meta metrics such as distribution of pac= ket sizes, inter-arrival times... Then use such information to label the tr= affic with the underlying application assuming that the granularity of clas= sification may vary (type of application, exact application name, version..= .) [Min-Suk Kim] continuously collect packet data,= then applying learning process for traffic classification with generating = application using deep learning models such as CNN (convolutional neural ne= twork) and RNN (recurrent neural network). Data-set to apply into the model= s are generated by processing with features of information from flow in pac= ket data. 2.6 TBD 3. Data Focus 3.1 Data attribute 3.2 Data format 3.3 TBD 4. Support Technologies 4.1 Benchmarking Framework Proposed by: pedro@nict.go.jp Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00146.html Abstract: A proper benchmarking framework comprises a se= t of reference procedures, methods, and models that can (or better *must*) = be followed to assess the quality of an AI mechanism proposed to be applied= to the network management/control area. Moreover, and much more specific t= o the IDNET topics, is the inclusion, dependency, or just the general relat= ion of a standard format enforced to the data that is used (input) and prod= uced (output) by the framework, so a kind of "data market" can arise withou= t requiring to transform the data. The initial scope of input/output data w= ould be the datasets, but also the new knowledge items that are stated as a= result of applying the benchmarking procedures defined by the framework, w= hich can be collected together to build a database of benchmark results, or= just contrasted with other existing entries in the database to know the po= sition of the solution just evaluated. This increases the usefulness of IDN= ET. 4.2 TBD --_000_6AE399511121AB42A34ACEF7BF25B4D298714CDGGEMM505MBXchina_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Hi Hesham, =

 

I think this question = seems simple but really significant. Please forgive if the answer was redun= dant. And it is welcome to point out my lacks if you have some different vi= ews.

 

In general, the new me= thod may bring potential benefits. There had been some discussions before i= n our mail list. Please search the keyword “benefit” and check = the result. But what I want to describe here are the following several points.

 

Firstly, in essence, t= he AI method can solve the scalability problem in each use case but existin= g methods cannot. In other words, the existing methods can hardly cover the= complexity along with the huge increment of service in the future by repeating the current solutions multi-times.&n= bsp; For example, if the cost (including time and economic) of configuring = a VPN is C, so that it will cost at least 100*C for 100 VPNs (this is ideal= , it is even worse in practice). Even though somebody may say the software programs can also provide the automat= ic solution for this problems, but the over-linear incremental complexity w= ill easily breakthrough the upper cost limit of  that we can pay. Howe= ver, since the AI method may solve the same question in yet another new way so that it may change the complexity = from over-linear growth to logarithmic growth (or even lower). This is sign= ificant because the current methods and their improved version can hardly i= mplement such a change. I think this is one of the core problems that AI solves. It builds up a new patter= n that can bear the pressure from new services and new requirements, which = the traditional method cannot support.

 

Secondly, the new meth= od can increase the automation level of the network but not only provide an= automatic program. The AI method can make more process automatic such as t= he decision and policy deploying. Some of them cannot be implemented by program. For example, if we plan to captu= re and modify a series of devices/networks configuration, how can we implem= ent that? The devices may be from different venders. The topology may be di= fferent. Their interfaces may be different. This situation is hard to solve by “if-else-then” l= ogic or very hard. However, if we can abstract the configuration and the po= licies with some unified expression, such as using standard 0-1 series or f= ormatted packet, the consulting between various devices will be simplified a lot. Of course, the “if-else-then”= ; logic can implement that but the core of problem is the complexity.

 

However, some of the c= ases should not be mentioned. The aim of IDNet is introducing AI to serve t= he Network but not Application. Therefore, any case, whose output implement= s only a new function or new application, may be not suitable (personal view). Because it might be hardly produced b= y existing method but it is only a new tool but does not serve the network.= We should focus on the case, whose output produces a decision, a strategy,= or a configuration relative with network/device, or the cases that directly serve the Network Management, e= specially improving the efficiency and decreasing the cost. They are valuab= le.  So the answer may not depend on whether the existing methods can = or cannot, but depends on the AI can or cannot provide a new form.

 

BTW, the new AI method= may perform “better” than existing solutions. But in my opinio= n, this “better” cannot become the sufficient condition to appl= y because it is often covered by the cost of deployment. 

 

In a word, in any case= s, the IDNet will provide a scalability solutions and increase the automati= on level that the existing method and automatic software programs cannot. A= nd our work here is to explore the standardization points and define the process, the data format, the architecture and etc.,= which support the compatibility for the various function entities. So that= make the AI method enlarge the power of the network.

 

 

Regards,

 

Yansen

 

From: = Hesham ElBakoury
Sent: Tuesday, August 15, 2017 8:49 PM
To: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 =

Hi Ya= nshen,

=  

In al= l of these use cases what problems we are trying to solve that have not bee= n solved before or why existing solutions are not good enough ?<= /span>

=  

Thank= s

=  

Hesha= m

=  

 =

Dear all,

 

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

 

If I miss something important, please let me know. A= pologized in advance.

 

Yansen

 

 

---------  Roadmap  ---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

---------  Roadmap End  ---------

 

 

1. Gap and Requirement Analysis

    1.1 Network Management requiremen= t

    1.2 TBD

2. Use Cases

    2.1 Traffic Prediction=

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Collect = the history traffic data and external data which may influence the traffic.= Predict the traffic in short/long/specific term. Avoid the congestion or r= isk in previously.

        &nbs= p;         

    2.2 QoS Management

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Use mult= iple paths to distribute the traffic flows. Adjust the percentages. Avoid c= ongestion and ensure QoS.

        

    2.3 Application (and/or DDoS= ) detection

        &nbs= p;          Proposed by: aydinulas@gmx.net

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html<= /o:p>

        &nbs= p;          Abstract: Detect t= he application (or attack) from network packets (HTTPS or plain) Collect th= e history traffic data and identify a service or attack (ex: Skype, Viber, = DDoS attack etc.)

        

         2.4= QoE Management

        &nbs= p;          Proposed by: albert.cabellos@gmail.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html<= /o:p>

        &nbs= p;          Abstract: Collect = low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then= use ML to understand what is the relation between satisfactory QoE and the= low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

        

         2.5= (Encrypted) Traffic Classification

        &nbs= p;          Proposed by: jerome.francois@inria.fr; mskim16= @etri.re.kr

        &nbs= p;          Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Mi= n-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html

        &nbs= p;          Abstract:

        &nbs= p;            &= nbsp;      [Jerome] collect flow-level traffic met= rics such as protocol information but also meta metrics such as distributio= n of packet sizes, inter-arrival times... Then use such information to labe= l the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)

        &nbs= p;            &= nbsp;      [Min-Suk Kim] continuously collect pack= et data, then applying learning process for traffic classification with gen= erating application using deep learning models such as CNN (convolutional n= eural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= o:p>

        &nbs= p;         

         2.6= TBD

        

3. Data Focus

    3.1 Data attribute

    3.2 Data format

    3.3 TBD

        

4. Support Technologies

    4.1 Benchmarking Framework

        &nbs= p;          Proposed by: pedro@nict.go.jp

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html<= /o:p>

        &nbs= p;          Abstract: A proper= benchmarking framework comprises a set of reference procedures, methods, a= nd models that can (or better *must*) be followed to assess the quality of = an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.=

        

    4.2 TBD

--_000_6AE399511121AB42A34ACEF7BF25B4D298714CDGGEMM505MBXchina_-- From nobody Wed Aug 16 14:20:30 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DFC62132710 for ; Wed, 16 Aug 2017 14:20:28 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.22 X-Spam-Level: X-Spam-Status: No, score=-4.22 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id EtUHP3xcacpe for ; Wed, 16 Aug 2017 14:20:25 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 75097132356 for ; Wed, 16 Aug 2017 14:20:24 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml704-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DTO47518; Wed, 16 Aug 2017 21:20:21 +0000 (GMT) Received: from SJCEML702-CHM.china.huawei.com (10.208.112.38) by lhreml704-cah.china.huawei.com (10.201.108.45) with Microsoft SMTP Server (TLS) id 14.3.301.0; Wed, 16 Aug 2017 22:20:20 +0100 Received: from SJCEML701-CHM.china.huawei.com ([169.254.3.191]) by SJCEML702-CHM.china.huawei.com ([169.254.4.148]) with mapi id 14.03.0301.000; Wed, 16 Aug 2017 14:20:10 -0700 From: Hesham ElBakoury To: yanshen CC: "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgBFDo4AABo54cAAKdXWkA== Date: Wed, 16 Aug 2017 21:20:10 +0000 Message-ID: References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D298714C@DGGEMM505-MBX.china.huawei.com> In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D298714C@DGGEMM505-MBX.china.huawei.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.139.86] Content-Type: multipart/alternative; boundary="_000_C3855D43D6701846AD1151A536E7A0582470475ASJCEML701CHMchi_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020203.5994B716.01FD, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.3.191, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 6e8070943f36dfba4ff02f5725b4f6b6 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 16 Aug 2017 21:20:29 -0000 --_000_C3855D43D6701846AD1151A536E7A0582470475ASJCEML701CHMchi_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Yansen, Thanks for your detailed response! There are also existing solutions which use AI/ML in (some of) these use ca= ses. How our AI solutions will be better than existing AI solutions. Let us take classification of encrypted traffic as an example. There are al= ready different solutions that use AI/ML to classify the encrypted traffic. What are the problems and shortcomings of these solutions that should be a= ddressed in new and better solutions which also use AI/ML ? Hesham From: yanshen Sent: Wednesday, August 16, 2017 7:26 AM To: Hesham ElBakoury Cc: idnet@ietf.org Subject: RE: Summary 20170814 & IDN dedicated session call for case Hi Hesham, I think this question seems simple but really significant. Please forgive i= f the answer was redundant. And it is welcome to point out my lacks if you = have some different views. In general, the new method may bring potential benefits. There had been som= e discussions before in our mail list. Please search the keyword "benefit" = and check the result. But what I want to describe here are the following se= veral points. Firstly, in essence, the AI method can solve the scalability problem in eac= h use case but existing methods cannot. In other words, the existing method= s can hardly cover the complexity along with the huge increment of service = in the future by repeating the current solutions multi-times. For example,= if the cost (including time and economic) of configuring a VPN is C, so th= at it will cost at least 100*C for 100 VPNs (this is ideal, it is even wors= e in practice). Even though somebody may say the software programs can also= provide the automatic solution for this problems, but the over-linear incr= emental complexity will easily breakthrough the upper cost limit of that w= e can pay. However, since the AI method may solve the same question in yet = another new way so that it may change the complexity from over-linear growt= h to logarithmic growth (or even lower). This is significant because the cu= rrent methods and their improved version can hardly implement such a change= . I think this is one of the core problems that AI solves. It builds up a n= ew pattern that can bear the pressure from new services and new requirement= s, which the traditional method cannot support. Secondly, the new method can increase the automation level of the network b= ut not only provide an automatic program. The AI method can make more proce= ss automatic such as the decision and policy deploying. Some of them cannot= be implemented by program. For example, if we plan to capture and modify a= series of devices/networks configuration, how can we implement that? The d= evices may be from different venders. The topology may be different. Their = interfaces may be different. This situation is hard to solve by "if-else-th= en" logic or very hard. However, if we can abstract the configuration and t= he policies with some unified expression, such as using standard 0-1 series= or formatted packet, the consulting between various devices will be simpli= fied a lot. Of course, the "if-else-then" logic can implement that but the = core of problem is the complexity. However, some of the cases should not be mentioned. The aim of IDNet is int= roducing AI to serve the Network but not Application. Therefore, any case, = whose output implements only a new function or new application, may be not = suitable (personal view). Because it might be hardly produced by existing m= ethod but it is only a new tool but does not serve the network. We should f= ocus on the case, whose output produces a decision, a strategy, or a config= uration relative with network/device, or the cases that directly serve the = Network Management, especially improving the efficiency and decreasing the = cost. They are valuable. So the answer may not depend on whether the exist= ing methods can or cannot, but depends on the AI can or cannot provide a ne= w form. BTW, the new AI method may perform "better" than existing solutions. But in= my opinion, this "better" cannot become the sufficient condition to apply = because it is often covered by the cost of deployment. In a word, in any cases, the IDNet will provide a scalability solutions and= increase the automation level that the existing method and automatic softw= are programs cannot. And our work here is to explore the standardization po= ints and define the process, the data format, the architecture and etc., wh= ich support the compatibility for the various function entities. So that ma= ke the AI method enlarge the power of the network. Regards, Yansen From: Hesham ElBakoury Sent: Tuesday, August 15, 2017 8:49 PM To: yanshen ; idnet@ietf.org Subject: RE: Summary 20170814 & IDN dedicated session call for case Hi Yanshen, In all of these use cases what problems we are trying to solve that have no= t been solved before or why existing solutions are not good enough ? Thanks Hesham From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M; mskim16@etri.re.kr Track: [Jerome] https://www.ietf.org/mail-archive/web/id= net/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive= /web/idnet/current/msg00153.html Abstract: [Jerome] collect flow-level traffic metrics suc= h as protocol information but also meta metrics such as distribution of pac= ket sizes, inter-arrival times... Then use such information to label the tr= affic with the underlying application assuming that the granularity of clas= sification may vary (type of application, exact application name, version..= .) [Min-Suk Kim] continuously collect packet data,= then applying learning process for traffic classification with generating = application using deep learning models such as CNN (convolutional neural ne= twork) and RNN (recurrent neural network). Data-set to apply into the model= s are generated by processing with features of information from flow in pac= ket data. 2.6 TBD 3. Data Focus 3.1 Data attribute 3.2 Data format 3.3 TBD 4. Support Technologies 4.1 Benchmarking Framework Proposed by: pedro@nict.go.jp Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00146.html Abstract: A proper benchmarking framework comprises a se= t of reference procedures, methods, and models that can (or better *must*) = be followed to assess the quality of an AI mechanism proposed to be applied= to the network management/control area. Moreover, and much more specific t= o the IDNET topics, is the inclusion, dependency, or just the general relat= ion of a standard format enforced to the data that is used (input) and prod= uced (output) by the framework, so a kind of "data market" can arise withou= t requiring to transform the data. The initial scope of input/output data w= ould be the datasets, but also the new knowledge items that are stated as a= result of applying the benchmarking procedures defined by the framework, w= hich can be collected together to build a database of benchmark results, or= just contrasted with other existing entries in the database to know the po= sition of the solution just evaluated. This increases the usefulness of IDN= ET. 4.2 TBD --_000_C3855D43D6701846AD1151A536E7A0582470475ASJCEML701CHMchi_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Hi Ya= nsen,

=  

Thank= s for your detailed response!

=  

There= are also existing solutions which use AI/ML in (some of) these use cases. = How our AI solutions will be better than existing AI solutions.<= /span>

Let u= s take classification of encrypted traffic as an example. There are already= different solutions that use AI/ML to classify the encrypted traffic.=

What = are the problems and shortcomings of these solutions  that should be a= ddressed in new and better solutions which also use AI/ML ?

=  

Hesha= m

=  

From: yanshen
Sent: Wednesday, August 16, 2017 7:26 AM
To: Hesham ElBakoury
Cc: idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 =

Hi Hesham, =

 

I think this question = seems simple but really significant. Please forgive if the answer was redun= dant. And it is welcome to point out my lacks if you have some different vi= ews.

 

In general, the new me= thod may bring potential benefits. There had been some discussions before i= n our mail list. Please search the keyword “benefit” and check = the result. But what I want to describe here are the following several points.

 

Firstly, in essence, t= he AI method can solve the scalability problem in each use case but existin= g methods cannot. In other words, the existing methods can hardly cover the= complexity along with the huge increment of service in the future by repeating the current solutions multi-times.&n= bsp; For example, if the cost (including time and economic) of configuring = a VPN is C, so that it will cost at least 100*C for 100 VPNs (this is ideal= , it is even worse in practice). Even though somebody may say the software programs can also provide the automat= ic solution for this problems, but the over-linear incremental complexity w= ill easily breakthrough the upper cost limit of  that we can pay. Howe= ver, since the AI method may solve the same question in yet another new way so that it may change the complexity = from over-linear growth to logarithmic growth (or even lower). This is sign= ificant because the current methods and their improved version can hardly i= mplement such a change. I think this is one of the core problems that AI solves. It builds up a new patter= n that can bear the pressure from new services and new requirements, which = the traditional method cannot support.

 

Secondly, the new meth= od can increase the automation level of the network but not only provide an= automatic program. The AI method can make more process automatic such as t= he decision and policy deploying. Some of them cannot be implemented by program. For example, if we plan to captu= re and modify a series of devices/networks configuration, how can we implem= ent that? The devices may be from different venders. The topology may be di= fferent. Their interfaces may be different. This situation is hard to solve by “if-else-then” l= ogic or very hard. However, if we can abstract the configuration and the po= licies with some unified expression, such as using standard 0-1 series or f= ormatted packet, the consulting between various devices will be simplified a lot. Of course, the “if-else-then”= ; logic can implement that but the core of problem is the complexity.

 

However, some of the c= ases should not be mentioned. The aim of IDNet is introducing AI to serve t= he Network but not Application. Therefore, any case, whose output implement= s only a new function or new application, may be not suitable (personal view). Because it might be hardly produced b= y existing method but it is only a new tool but does not serve the network.= We should focus on the case, whose output produces a decision, a strategy,= or a configuration relative with network/device, or the cases that directly serve the Network Management, e= specially improving the efficiency and decreasing the cost. They are valuab= le.  So the answer may not depend on whether the existing methods can = or cannot, but depends on the AI can or cannot provide a new form.

 

BTW, the new AI method= may perform “better” than existing solutions. But in my opinio= n, this “better” cannot become the sufficient condition to appl= y because it is often covered by the cost of deployment. 

 

In a word, in any case= s, the IDNet will provide a scalability solutions and increase the automati= on level that the existing method and automatic software programs cannot. A= nd our work here is to explore the standardization points and define the process, the data format, the architecture and etc.,= which support the compatibility for the various function entities. So that= make the AI method enlarge the power of the network.

 

 

Regards,

 

Yansen

 

From: = Hesham ElBakoury
Sent: Tuesday, August 15, 2017 8:49 PM
To: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 =

Hi Ya= nshen,

=  

In al= l of these use cases what problems we are trying to solve that have not bee= n solved before or why existing solutions are not good enough ?<= /span>

=  

Thank= s

=  

Hesha= m

=  

From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

 =

Dear all,

 

Here is a summary and some index (2017.08.14). Till = now, whatever the case is supported or not, I tried to organize all the con= tent and keep the core part. It is still welcome to contribute and discuss.=

 

If I miss something important, please let me know. A= pologized in advance.

 

Yansen

 

 

---------  Roadmap  ---------

***Aug. : Collecting the use cases (related with NM)= . Rough thoughts and requirements

Sep. : Refining the cases and abstract the common el= ements

Oct. : Deeply analysis. Especially on Data Format, c= ontrol flow, or other key points

Nov.: F2F discussions on IETF100

---------  Roadmap End  ---------

 

 

1. Gap and Requirement Analysis

    1.1 Network Management requiremen= t

    1.2 TBD

2. Use Cases

    2.1 Traffic Prediction=

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Collect = the history traffic data and external data which may influence the traffic.= Predict the traffic in short/long/specific term. Avoid the congestion or r= isk in previously.

        &nbs= p;         

    2.2 QoS Management

        &nbs= p;          Proposed by: yanshen@huawei.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

        &nbs= p;          Abstract: Use mult= iple paths to distribute the traffic flows. Adjust the percentages. Avoid c= ongestion and ensure QoS.

        

    2.3 Application (and/or DDoS= ) detection

        &nbs= p;          Proposed by: aydinulas@gmx.net

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html<= /o:p>

        &nbs= p;          Abstract: Detect t= he application (or attack) from network packets (HTTPS or plain) Collect th= e history traffic data and identify a service or attack (ex: Skype, Viber, = DDoS attack etc.)

        

         2.4= QoE Management

        &nbs= p;          Proposed by: albert.cabellos@gmail.com

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html<= /o:p>

        &nbs= p;          Abstract: Collect = low-level metrics (SNR, latency, jitter, losses, etc) and measure QoE. Then= use ML to understand what is the relation between satisfactory QoE and the= low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N then QoE is sati= sfactory for the customers (please note that QoE cannot be measured directl= y over your network). This is useful to understand how the network must be = operated to provide satisfactory QoE.

        

         2.5= (Encrypted) Traffic Classification

        &nbs= p;          Proposed by: jerome.francois@inria.fr; mskim16= @etri.re.kr

        &nbs= p;          Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Mi= n-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html

        &nbs= p;          Abstract:

        &nbs= p;            &= nbsp;      [Jerome] collect flow-level traffic met= rics such as protocol information but also meta metrics such as distributio= n of packet sizes, inter-arrival times... Then use such information to labe= l the traffic with the underlying application assuming that the granularity of classification= may vary (type of application, exact application name, version...)

        &nbs= p;            &= nbsp;      [Min-Suk Kim] continuously collect pack= et data, then applying learning process for traffic classification with gen= erating application using deep learning models such as CNN (convolutional n= eural network) and RNN (recurrent neural network). Data-set to apply into the models are gene= rated by processing with features of information from flow in packet data.<= o:p>

        &nbs= p;         

         2.6= TBD

        

3. Data Focus

    3.1 Data attribute

    3.2 Data format

    3.3 TBD

        

4. Support Technologies

    4.1 Benchmarking Framework

        &nbs= p;          Proposed by: pedro@nict.go.jp

        &nbs= p;          Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html<= /o:p>

        &nbs= p;          Abstract: A proper= benchmarking framework comprises a set of reference procedures, methods, a= nd models that can (or better *must*) be followed to assess the quality of = an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET top= ics, is the inclusion, dependency, or just the general relation of a standa= rd format enforced to the data that is used (input) and produced (output) b= y the framework, so a kind of "data market" can arise without requiring to transform the data. The initia= l scope of input/output data would be the datasets, but also the new knowle= dge items that are stated as a result of applying the benchmarking procedur= es defined by the framework, which can be collected together to build a database of benchmark results, or just co= ntrasted with other existing entries in the database to know the position o= f the solution just evaluated. This increases the usefulness of IDNET.=

        

    4.2 TBD

--_000_C3855D43D6701846AD1151A536E7A0582470475ASJCEML701CHMchi_-- From nobody Wed Aug 16 19:00:54 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 8734A1321F0 for ; Wed, 16 Aug 2017 19:00:53 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.999 X-Spam-Level: X-Spam-Status: No, score=-1.999 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id w7oF-GhEUkWn for ; Wed, 16 Aug 2017 19:00:51 -0700 (PDT) Received: from mail-ua0-x22a.google.com (mail-ua0-x22a.google.com [IPv6:2607:f8b0:400c:c08::22a]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id A3E1913217D for ; Wed, 16 Aug 2017 19:00:51 -0700 (PDT) Received: by mail-ua0-x22a.google.com with SMTP id k43so20223788uaf.3 for ; Wed, 16 Aug 2017 19:00:51 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:from:date:message-id:subject:to; bh=kKK3CNmO26lpbTIUf8MazrMyA2t7/GXrNcAk/3Cq/gE=; b=B/gh9/PMf89PYPKRxPNL+93cCSAv/vozXlNugd+Fv1/DdMHrAVK/wTtYB2RDrCxjYJ Fti3mw5ttRMrsIXS01CkOVBiLfkf/Gf0l+tnXyOkVa3bSy29No8qlyvreRizOWd8+u1J veiNLN0Pvsf2wy8aZ5KOMUS0mDxB6qQHCauyZAymKOjh48v+O/ZCw5oqe0ifJEx6mgrU ZQpCZ3BzVChidW2RMy/43mo5+uegCilu0DrKJqaX5Ie5itawUeXTW6c5HkseeOT4Znrz 7RaSII2ZTNu4yhMAfhavuufJzWSQDhsdkGux0RKc+eZkQ6x4iXySMMlZBQzHQNdITFxE nTvQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:from:date:message-id:subject:to; bh=kKK3CNmO26lpbTIUf8MazrMyA2t7/GXrNcAk/3Cq/gE=; b=L4aLZAJHcy+vhtr459NC7NlLo3wB7VGIbI0cE06JKrUyNy2XNYRVdZFw18Yp+yhd7Z 1rNC1l1YefTPJCl+7Ar+FCrgV3Ge5ksWcwf72OHkZtpWoYuiwLxXqJ/LL6MqaP++m0Tg XeSgA93UQssvWqGKpIOqk7f5v9/2f3/mzh4jX6DHtjwUvlrh6vALZrTCWmPjQSpsPE3n LIvp+kuRgYTwrBz7aIRlapZggX2Oot/R0U0rn5RlSr34sxPYUsWrIpJcMrI2hsfh5nEj PwlTjn+QgO+mjq82lWL9UU3NRZKlaw/f2Yc7o0Yhb3/7Esafo+TrUQRax998HGbXXQU4 Ktmw== X-Gm-Message-State: AHYfb5iLCb5fV1uovWSeQWlxOIfTlRnO0Zn3V28d4xKP6ITxnc2diuTO E0VuwuO2fGHCVMC+n/YioVdyFyQ5ZcMf X-Received: by 10.176.83.142 with SMTP id k14mr2017097uaa.14.1502935250383; Wed, 16 Aug 2017 19:00:50 -0700 (PDT) MIME-Version: 1.0 Received: by 10.176.82.129 with HTTP; Wed, 16 Aug 2017 19:00:50 -0700 (PDT) From: Mahesh Govind Date: Thu, 17 Aug 2017 07:30:50 +0530 Message-ID: To: idnet@ietf.org Content-Type: multipart/alternative; boundary="f403045e2274b837660556e961bd" Archived-At: Subject: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 17 Aug 2017 02:00:54 -0000 --f403045e2274b837660556e961bd Content-Type: text/plain; charset="UTF-8" Hi, Could you please give me some pointers to public dataset , which could be used for experimenting . Or If any one is willing to share dataset it will be great ... regards mahesh --f403045e2274b837660556e961bd Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi,

Could you please give me some point= ers to public dataset , which could be used for experimenting .
O= r
=C2=A0If any one is willing to share dataset =C2=A0it will be g= reat ...

regards
mahesh

--f403045e2274b837660556e961bd-- From nobody Wed Aug 16 19:09:47 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 7FE311321F0 for ; Wed, 16 Aug 2017 19:09:46 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.398 X-Spam-Level: X-Spam-Status: No, score=-2.398 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, FREEMAIL_FORGED_FROMDOMAIN=0.199, FREEMAIL_FROM=0.001, HEADER_FROM_DIFFERENT_DOMAINS=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 8OydLawYhw4s for ; Wed, 16 Aug 2017 19:09:44 -0700 (PDT) Received: from mail-yw0-x232.google.com (mail-yw0-x232.google.com [IPv6:2607:f8b0:4002:c05::232]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D2BF1132359 for ; Wed, 16 Aug 2017 19:09:43 -0700 (PDT) Received: by mail-yw0-x232.google.com with SMTP id n83so5461018ywn.2 for ; Wed, 16 Aug 2017 19:09:43 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:sender:in-reply-to:references:from:date:message-id :subject:to:cc; bh=oCwdXWPKGjMST7NLFc8L/W4o5gTaO3arJ5qTT4lzDvU=; b=Hq+xXt2ZOef4l/lmvXVquNFFICY3JHFX4fSbvwCzkMKEuDSK83WAnre7alWzkTlNVN /DYLaioSn7eDdiDGNCmIHwpdbEZdD7j9kRlnWFVcPMXsN+DN1vSmyqgKZa+bng/N3TNu /OdBfD0kQE+JIZ6DYN3KbPL2+saacA2gBPu0sjI805tHtUVdSE6u0Cn05vmSV0aDUgYF vIIZJOwlvbtC88IcD7fHimxLdv8tihQuAx+0oV5x4K9/y5Id8xY+IdK9FjFwpS3FuVQz k6/YbcnmQPeZgEnnBdboWnAFBZ96TfeenU8fuWocjn/jNvm8k7lp6SpoBZLLTD+lOoJa Fl2g== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:sender:in-reply-to:references:from :date:message-id:subject:to:cc; bh=oCwdXWPKGjMST7NLFc8L/W4o5gTaO3arJ5qTT4lzDvU=; b=VcmsQ4RluzwEy3JT4p1wMuwqtlHcV0Xf04f11SXN3eYz7FVaiq0kRFJ4jrqZfkD5MC Za1Rabj6z/UNndSDwnFb3lnkkIUrbMP0w3828bv6twibhylctD5YPVSWYNAlROJika/0 6wcMWF4DGH173MeAlpW4vpQDBXVcaG8IhwU5eOYrf1AtRmenB5IP4iTSFE8kW6x1GK8b EJhrMCiwOahnt+KAOwGIXv7/dxf+fV5IqxRKFOOZ49aBA5hv/JnBhJseE+Xda+3kn6yC 3UwzrR4ZQnrUcuCz2sNu3RnDQQVmurzmOyyWcoS5adNb1GnR3zY/2QSd+vB3LmnM/N0D 3Hcg== X-Gm-Message-State: AHYfb5gzlncbpi5NthN+581OrZu3YKuz2GlMk/zNkPP8XF0aGF9p/hTf 2LKTFFddTnbEH9n8X5Kk6TMqU92brQ== X-Received: by 10.13.206.132 with SMTP id q126mr2889944ywd.337.1502935783182; Wed, 16 Aug 2017 19:09:43 -0700 (PDT) MIME-Version: 1.0 Sender: steniofernandes@gmail.com Received: by 10.37.104.74 with HTTP; Wed, 16 Aug 2017 19:09:41 -0700 (PDT) Received: by 10.37.104.74 with HTTP; Wed, 16 Aug 2017 19:09:41 -0700 (PDT) In-Reply-To: References: From: Stenio Fernandes Date: Wed, 16 Aug 2017 22:09:41 -0400 X-Google-Sender-Auth: OV8GZ3bAXiBFITE4dD22Kql1a3E Message-ID: To: Mahesh Govind Cc: idnet@ietf.org Content-Type: multipart/alternative; boundary="001a114e4c467a18f90556e98152" Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 17 Aug 2017 02:09:46 -0000 --001a114e4c467a18f90556e98152 Content-Type: text/plain; charset="UTF-8" http://crawdad.org https://www.caida.org/data/ http://imdc.datcat.org/Home On Aug 16, 2017 22:01, "Mahesh Govind" wrote: > Hi, > > Could you please give me some pointers to public dataset , which could be > used for experimenting . > Or > If any one is willing to share dataset it will be great ... > > regards > mahesh > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --001a114e4c467a18f90556e98152 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
http://c= rawdad.org

On Aug 16, 2017 22:01, "Mahesh Govind" <vu3mmg@gmail.com> wrote:
Hi,

Could you please give me some pointers to public dataset , which c= ould be used for experimenting .
Or
=C2=A0If any one is= willing to share dataset =C2=A0it will be great ...

regards
mahesh


_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

--001a114e4c467a18f90556e98152-- From nobody Wed Aug 16 19:13:18 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 228481321F0 for ; Wed, 16 Aug 2017 19:13:18 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.9 X-Spam-Level: X-Spam-Status: No, score=-1.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id xdgae3BYxX8M for ; Wed, 16 Aug 2017 19:13:16 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id 3D73713217D for ; Wed, 16 Aug 2017 19:13:16 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v7H2DEj7054744; Thu, 17 Aug 2017 11:13:14 +0900 (JST) Received: from mail2.nict.go.jp (mail2.nict.go.jp [133.243.18.15]) by gw2.nict.go.jp with ESMTP id v7H2DD7h054740; Thu, 17 Aug 2017 11:13:13 +0900 (JST) Received: from spectre (unknown [133.243.115.68]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail2.nict.go.jp (NICT Mail Spool Server2) with ESMTPS id BEA6FCF4C; Thu, 17 Aug 2017 11:13:13 +0900 (JST) Date: Thu, 17 Aug 2017 11:13:14 +0900 From: Pedro Martinez-Julia To: Mahesh Govind Cc: idnet@ietf.org Message-ID: <20170817021314.GC6804@spectre> References: MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.3 (2017-05-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 17 Aug 2017 02:13:18 -0000 On Thu, Aug 17, 2017 at 07:30:50AM +0530, Mahesh Govind wrote: > Hi, > > Could you please give me some pointers to public dataset , which could be > used for experimenting . > Or > If any one is willing to share dataset it will be great ... As you can find in this mailing list, a key objective of IDNET is precisely to find/get/gather proper datasets that can be used for management research and AI training. This means that we still do not have them, so we cannot provide them to you. In case you find some well-formed and somehow validated dataset elsewhere, I encourage you to please contribute it to the IDNET community. Thank you very much. > regards > mahesh Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Wed Aug 16 19:21:14 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 102441323AF for ; Wed, 16 Aug 2017 19:21:12 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.998 X-Spam-Level: X-Spam-Status: No, score=-1.998 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id iUDVwSRNIYJS for ; Wed, 16 Aug 2017 19:21:09 -0700 (PDT) Received: from mail-vk0-x236.google.com (mail-vk0-x236.google.com [IPv6:2607:f8b0:400c:c05::236]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 3FAEA13241A for ; Wed, 16 Aug 2017 19:21:09 -0700 (PDT) Received: by mail-vk0-x236.google.com with SMTP id u133so18136364vke.3 for ; Wed, 16 Aug 2017 19:21:09 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=mrv+YttsTZEL3cuGiiL4JnZFfxNPwG+mgJFkcXp4Iks=; b=Sm1NoRrwSLJP+saih0vj2Olv2Ag9irPPvgArIp8W0gF6wl9zLmNvW9MeONAIZ84rBA seXFl468XxrO2oLZymbGpqRzXantBbN2sE0lpKuhfYgpg4BZ6AGI0i/0yfknqdyWNO+g GzUXkKqkymtO0089UdIz/e+Qr01W7kQdSPj9hlIq8nuuWjcd2qMhck4jrN2G6vyLFAT9 N8tWmKh20wkR4QMShWVgodOOFtDtYCLULNl/YFoCi+m4kbwZcvTB+SNyat9hXkbay5XY aKl708+3XbnTQlj+vk2j8W92NyaKX0LZW498irMmSleQ/6eCq88kWDsIBKEa2CPRHB3t L/kw== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=mrv+YttsTZEL3cuGiiL4JnZFfxNPwG+mgJFkcXp4Iks=; b=cK9wKYIen48ezrXjKrecbk74HVwTpUg/YdEWXF8w7KZquw67XE6xzEpr1NQ+wxm375 z//5+X0ZukH1E/wCIaCU5pOtFxyz1T3HQ9d/i4fLFd7x+TjL/+5cmzgr+oGwyAksnrba Y1BeUQZqUqtg/NgU40B+mG3VlwD4rz6OqjjtBZcu1T7Acs1Pnhh2f5Dl6z71nKkdUYQs qNB0bfQY1C+2XqgsIjzPFx9ycpyyadPCX+3vLUKMv0Id2yaMpMey/XosEN0szX7SEJHa AG/2cMIbXcOMVMmajsnUKuTa/RIXG/SUGq0NNAdyrnoiPE31xFhbYVStVzdnqN3Ug/qq IjAA== X-Gm-Message-State: AHYfb5iGUqzz8dBvD8xC6DQkRdNxvao/7eBYvEK32DygdLvkspCrG1Nb 6FI3bi0SEifNeHMDd7wEd9Dj95JaBg== X-Received: by 10.31.63.151 with SMTP id m145mr2303508vka.59.1502936468273; Wed, 16 Aug 2017 19:21:08 -0700 (PDT) MIME-Version: 1.0 Received: by 10.176.82.129 with HTTP; Wed, 16 Aug 2017 19:21:07 -0700 (PDT) In-Reply-To: <20170817021314.GC6804@spectre> References: <20170817021314.GC6804@spectre> From: Mahesh Govind Date: Thu, 17 Aug 2017 07:51:07 +0530 Message-ID: To: Pedro Martinez-Julia Cc: idnet@ietf.org Content-Type: multipart/alternative; boundary="001a114dbdf64fc1e80556e9aab8" Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 17 Aug 2017 02:21:12 -0000 --001a114dbdf64fc1e80556e9aab8 Content-Type: text/plain; charset="UTF-8" Thank you Stenio . Thank you Pedro . I was exploring whether we could get some pointers . Stenio , sent some links . I am sure , collectively we can catalog the publicly available one . I did a bit of googling and found the following quora question . https://www.quora.com/Where-can-I-get-the-latest-dataset-for-a-network-intrusion-detection-system On Thu, Aug 17, 2017 at 7:43 AM, Pedro Martinez-Julia wrote: > On Thu, Aug 17, 2017 at 07:30:50AM +0530, Mahesh Govind wrote: > > Hi, > > > > Could you please give me some pointers to public dataset , which could be > > used for experimenting . > > Or > > If any one is willing to share dataset it will be great ... > > As you can find in this mailing list, a key objective of IDNET is > precisely to find/get/gather proper datasets that can be used for > management research and AI training. This means that we still do not > have them, so we cannot provide them to you. In case you find some > well-formed and somehow validated dataset elsewhere, I encourage you to > please contribute it to the IDNET community. Thank you very much. > > > regards > > mahesh > > Regards, > Pedro > > -- > Pedro Martinez-Julia > Network Science and Convergence Device Technology Laboratory > Network System Research Institute > National Institute of Information and Communications Technology (NICT) > 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan > Email: pedro@nict.go.jp > --------------------------------------------------------- > *** Entia non sunt multiplicanda praeter necessitatem *** > --001a114dbdf64fc1e80556e9aab8 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Thank you Stenio .

Thank you Pedro . I = was exploring whether we could get some pointers . Stenio , sent some links= .
I am sure , collectively we can catalog the publicly available= one .

I did a bit of googling =C2=A0and found the= following quora question .




On Thu,= Aug 17, 2017 at 7:43 AM, Pedro Martinez-Julia <pedro@nict.go.jp> wrote:
On Thu, Aug = 17, 2017 at 07:30:50AM +0530, Mahesh Govind wrote:
> Hi,
>
> Could you please give me some pointers to public dataset , which could= be
> used for experimenting .
> Or
>=C2=A0 If any one is willing to share dataset=C2=A0 it will be great ..= .

As you can find in this mailing list, a key objective of IDNET is precisely to find/get/gather proper datasets that can be used for
management research and AI training. This means that we still do not
have them, so we cannot provide them to you. In case you find some
well-formed and somehow validated dataset elsewhere, I encourage you to
please contribute it to the IDNET community. Thank you very much.

> regards
> mahesh

Regards,
Pedro

--
Pedro Martinez-Julia
Network Science and Convergence Device Technology Laboratory
Network System Research Institute
National Institute of Information and Communications Technology (NICT)
4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
Email: pedro@nict.go.jp
---------------------------------------------------------
*** Entia non sunt multiplicanda praeter necessitatem ***

--001a114dbdf64fc1e80556e9aab8-- From nobody Wed Aug 16 21:09:57 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id F006A1320BE for ; Wed, 16 Aug 2017 21:09:51 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.219 X-Spam-Level: X-Spam-Status: No, score=-4.219 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 8AaWLARbUIP6 for ; Wed, 16 Aug 2017 21:09:49 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 719CB1323B3 for ; Wed, 16 Aug 2017 21:09:48 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml701-cah.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DMU11216; Thu, 17 Aug 2017 04:09:46 +0000 (GMT) Received: from DGGEMM405-HUB.china.huawei.com (10.3.20.213) by lhreml701-cah.china.huawei.com (10.201.108.42) with Microsoft SMTP Server (TLS) id 14.3.301.0; Thu, 17 Aug 2017 05:09:44 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.229]) by DGGEMM405-HUB.china.huawei.com ([10.3.20.213]) with mapi id 14.03.0301.000; Thu, 17 Aug 2017 12:09:20 +0800 From: yanshen To: Hesham ElBakoury CC: "idnet@ietf.org" Thread-Topic: Summary 20170814 & IDN dedicated session call for case Thread-Index: AdMUrc8Ikmqfd+z8QxaI3D4f5YkgmgBFDo4AABo54cAAKdXWkAAOmmWA Date: Thu, 17 Aug 2017 04:09:19 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D29872B1@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D298714C@DGGEMM505-MBX.china.huawei.com> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.130.179.89] Content-Type: multipart/alternative; boundary="_000_6AE399511121AB42A34ACEF7BF25B4D29872B1DGGEMM505MBXchina_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090205.5995170A.00D9, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.229, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 43a6cfdcfe6e42dbffe3f653857679b4 Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 17 Aug 2017 04:09:52 -0000 --_000_6AE399511121AB42A34ACEF7BF25B4D29872B1DGGEMM505MBXchina_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Hesham, Personal view. It is not important that whether the existing traffic recogn= izing methods are accurate or whether the problems are serious. We should f= ocus on the previous and following steps. Whether we can abstract the data = or process becoming a standard format? And how to transmit the result into = the form that can be used in the following configuring process. Yansen From: Hesham ElBakoury Sent: Thursday, August 17, 2017 5:20 AM To: yanshen Cc: idnet@ietf.org Subject: RE: Summary 20170814 & IDN dedicated session call for case Hi Yansen, Thanks for your detailed response! There are also existing solutions which use AI/ML in (some of) these use ca= ses. How our AI solutions will be better than existing AI solutions. Let us take classification of encrypted traffic as an example. There are al= ready different solutions that use AI/ML to classify the encrypted traffic. What are the problems and shortcomings of these solutions that should be a= ddressed in new and better solutions which also use AI/ML ? Hesham From: yanshen Sent: Wednesday, August 16, 2017 7:26 AM To: Hesham ElBakoury Cc: idnet@ietf.org Subject: RE: Summary 20170814 & IDN dedicated session call for case Hi Hesham, I think this question seems simple but really significant. Please forgive i= f the answer was redundant. And it is welcome to point out my lacks if you = have some different views. In general, the new method may bring potential benefits. There had been som= e discussions before in our mail list. Please search the keyword "benefit" = and check the result. But what I want to describe here are the following se= veral points. Firstly, in essence, the AI method can solve the scalability problem in eac= h use case but existing methods cannot. In other words, the existing method= s can hardly cover the complexity along with the huge increment of service = in the future by repeating the current solutions multi-times. For example,= if the cost (including time and economic) of configuring a VPN is C, so th= at it will cost at least 100*C for 100 VPNs (this is ideal, it is even wors= e in practice). Even though somebody may say the software programs can also= provide the automatic solution for this problems, but the over-linear incr= emental complexity will easily breakthrough the upper cost limit of that w= e can pay. However, since the AI method may solve the same question in yet = another new way so that it may change the complexity from over-linear growt= h to logarithmic growth (or even lower). This is significant because the cu= rrent methods and their improved version can hardly implement such a change= . I think this is one of the core problems that AI solves. It builds up a n= ew pattern that can bear the pressure from new services and new requirement= s, which the traditional method cannot support. Secondly, the new method can increase the automation level of the network b= ut not only provide an automatic program. The AI method can make more proce= ss automatic such as the decision and policy deploying. Some of them cannot= be implemented by program. For example, if we plan to capture and modify a= series of devices/networks configuration, how can we implement that? The d= evices may be from different venders. The topology may be different. Their = interfaces may be different. This situation is hard to solve by "if-else-th= en" logic or very hard. However, if we can abstract the configuration and t= he policies with some unified expression, such as using standard 0-1 series= or formatted packet, the consulting between various devices will be simpli= fied a lot. Of course, the "if-else-then" logic can implement that but the = core of problem is the complexity. However, some of the cases should not be mentioned. The aim of IDNet is int= roducing AI to serve the Network but not Application. Therefore, any case, = whose output implements only a new function or new application, may be not = suitable (personal view). Because it might be hardly produced by existing m= ethod but it is only a new tool but does not serve the network. We should f= ocus on the case, whose output produces a decision, a strategy, or a config= uration relative with network/device, or the cases that directly serve the = Network Management, especially improving the efficiency and decreasing the = cost. They are valuable. So the answer may not depend on whether the exist= ing methods can or cannot, but depends on the AI can or cannot provide a ne= w form. BTW, the new AI method may perform "better" than existing solutions. But in= my opinion, this "better" cannot become the sufficient condition to apply = because it is often covered by the cost of deployment. In a word, in any cases, the IDNet will provide a scalability solutions and= increase the automation level that the existing method and automatic softw= are programs cannot. And our work here is to explore the standardization po= ints and define the process, the data format, the architecture and etc., wh= ich support the compatibility for the various function entities. So that ma= ke the AI method enlarge the power of the network. Regards, Yansen From: Hesham ElBakoury Sent: Tuesday, August 15, 2017 8:49 PM To: yanshen >; idnet@ietf.org= Subject: RE: Summary 20170814 & IDN dedicated session call for case Hi Yanshen, In all of these use cases what problems we are trying to solve that have no= t been solved before or why existing solutions are not good enough ? Thanks Hesham From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Sunday, August 13, 2017 8:36 PM To: idnet@ietf.org Subject: [Idnet] Summary 20170814 & IDN dedicated session call for case Dear all, Here is a summary and some index (2017.08.14). Till now, whatever the case = is supported or not, I tried to organize all the content and keep the core = part. It is still welcome to contribute and discuss. If I miss something important, please let me know. Apologized in advance. Yansen --------- Roadmap --------- ***Aug. : Collecting the use cases (related with NM). Rough thoughts and re= quirements Sep. : Refining the cases and abstract the common elements Oct. : Deeply analysis. Especially on Data Format, control flow, or other k= ey points Nov.: F2F discussions on IETF100 --------- Roadmap End --------- 1. Gap and Requirement Analysis 1.1 Network Management requirement 1.2 TBD 2. Use Cases 2.1 Traffic Prediction Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Collect the history traffic data and external = data which may influence the traffic. Predict the traffic in short/long/spe= cific term. Avoid the congestion or risk in previously. 2.2 QoS Management Proposed by: yanshen@huawei.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00131.html Abstract: Use multiple paths to distribute the traffic f= lows. Adjust the percentages. Avoid congestion and ensure QoS. 2.3 Application (and/or DDoS) detection Proposed by: aydinulas@gmx.net Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00133.html Abstract: Detect the application (or attack) from networ= k packets (HTTPS or plain) Collect the history traffic data and identify a = service or attack (ex: Skype, Viber, DDoS attack etc.) 2.4 QoE Management Proposed by: albert.cabellos@gmail.com Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00137.html Abstract: Collect low-level metrics (SNR, latency, jitte= r, losses, etc) and measure QoE. Then use ML to understand what is the rela= tion between satisfactory QoE and the low-level metrics. As an example lear= n that when delay>N then QoE is degraded, but when M; mskim16@etri.re.kr Track: [Jerome] https://www.ietf.org/mail-archive/web/id= net/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive= /web/idnet/current/msg00153.html Abstract: [Jerome] collect flow-level traffic metrics suc= h as protocol information but also meta metrics such as distribution of pac= ket sizes, inter-arrival times... Then use such information to label the tr= affic with the underlying application assuming that the granularity of clas= sification may vary (type of application, exact application name, version..= .) [Min-Suk Kim] continuously collect packet data,= then applying learning process for traffic classification with generating = application using deep learning models such as CNN (convolutional neural ne= twork) and RNN (recurrent neural network). Data-set to apply into the model= s are generated by processing with features of information from flow in pac= ket data. 2.6 TBD 3. Data Focus 3.1 Data attribute 3.2 Data format 3.3 TBD 4. Support Technologies 4.1 Benchmarking Framework Proposed by: pedro@nict.go.jp Track: https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00146.html Abstract: A proper benchmarking framework comprises a se= t of reference procedures, methods, and models that can (or better *must*) = be followed to assess the quality of an AI mechanism proposed to be applied= to the network management/control area. Moreover, and much more specific t= o the IDNET topics, is the inclusion, dependency, or just the general relat= ion of a standard format enforced to the data that is used (input) and prod= uced (output) by the framework, so a kind of "data market" can arise withou= t requiring to transform the data. The initial scope of input/output data w= ould be the datasets, but also the new knowledge items that are stated as a= result of applying the benchmarking procedures defined by the framework, w= hich can be collected together to build a database of benchmark results, or= just contrasted with other existing entries in the database to know the po= sition of the solution just evaluated. This increases the usefulness of IDN= ET. 4.2 TBD --_000_6AE399511121AB42A34ACEF7BF25B4D29872B1DGGEMM505MBXchina_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Hi Hesh= am,

&n= bsp;

Persona= l view. It is not important that whether the existing traffic recognizing m= ethods are accurate or whether the problems are serious. We should focus on= the previous and following steps. Whether we can abstract the data or process becoming a standard format? And how to= transmit the result into the form that can be used in the following config= uring process.

&n= bsp;

Yansen<= o:p>

&n= bsp;

From: Hesham ElBakoury
Sent: Thursday, August 17, 2017 5:20 AM
To: yanshen <yanshen@huawei.com>
Cc: idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 

Hi Yansen,

 

Thanks for your detailed response!

 

There are also existing solutions which use AI/ML in (some of) th= ese use cases. How our AI solutions will be better than existing AI solutio= ns.

Let us take classification of encrypted traffic as an example. Th= ere are already different solutions that use AI/ML to classify the encrypte= d traffic.

What are the problems and shortcomings of these solutions  t= hat should be addressed in new and better solutions which also use AI/ML ?<= o:p>

 

Hesham

 

From: yanshen
Sent: Wednesday, August 16, 2017 7:26 AM
To: Hesham ElBakoury
Cc: idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 

Hi Hesh= am,

&n= bsp;

I think= this question seems simple but really significant. Please forgive if the a= nswer was redundant. And it is welcome to point out my lacks if you have so= me different views.

&n= bsp;

In gene= ral, the new method may bring potential benefits. There had been some discu= ssions before in our mail list. Please search the keyword “benefit= 221; and check the result. But what I want to describe here are the following several points.

&n= bsp;

Firstly= , in essence, the AI method can solve the scalability problem in each use c= ase but existing methods cannot. In other words, the existing methods can h= ardly cover the complexity along with the huge increment of service in the future by repeating the current solut= ions multi-times.  For example, if the cost (including time and econom= ic) of configuring a VPN is C, so that it will cost at least 100*C for 100 = VPNs (this is ideal, it is even worse in practice). Even though somebody may say the software programs can also = provide the automatic solution for this problems, but the over-linear incre= mental complexity will easily breakthrough the upper cost limit of  th= at we can pay. However, since the AI method may solve the same question in yet another new way so that it may c= hange the complexity from over-linear growth to logarithmic growth (or even= lower). This is significant because the current methods and their improved= version can hardly implement such a change. I think this is one of the core problems that AI solves. It buil= ds up a new pattern that can bear the pressure from new services and new re= quirements, which the traditional method cannot support.

&n= bsp;

Secondl= y, the new method can increase the automation level of the network but not = only provide an automatic program. The AI method can make more process auto= matic such as the decision and policy deploying. Some of them cannot be implemented by program. For example, if = we plan to capture and modify a series of devices/networks configuration, h= ow can we implement that? The devices may be from different venders. The to= pology may be different. Their interfaces may be different. This situation is hard to solve by “if-else-then&#= 8221; logic or very hard. However, if we can abstract the configuration and= the policies with some unified expression, such as using standard 0-1 seri= es or formatted packet, the consulting between various devices will be simplified a lot. Of course, the “if-else-th= en” logic can implement that but the core of problem is the complexit= y.

&n= bsp;

However= , some of the cases should not be mentioned. The aim of IDNet is introducin= g AI to serve the Network but not Application. Therefore, any case, whose o= utput implements only a new function or new application, may be not suitable (personal view). Because it might be = hardly produced by existing method but it is only a new tool but does not s= erve the network. We should focus on the case, whose output produces a deci= sion, a strategy, or a configuration relative with network/device, or the cases that directly serve the Network= Management, especially improving the efficiency and decreasing the cost. T= hey are valuable.  So the answer may not depend on whether the existin= g methods can or cannot, but depends on the AI can or cannot provide a new form.

&n= bsp;

BTW, th= e new AI method may perform “better” than existing solutions. B= ut in my opinion, this “better” cannot become the sufficient co= ndition to apply because it is often covered by the cost of deployment.&nbs= p;

&n= bsp;

In a wo= rd, in any cases, the IDNet will provide a scalability solutions and increa= se the automation level that the existing method and automatic software pro= grams cannot. And our work here is to explore the standardization points and define the process, the data format= , the architecture and etc., which support the compatibility for the variou= s function entities. So that make the AI method enlarge the power of the ne= twork.

&n= bsp;

&n= bsp;

Regards= ,

&n= bsp;

Yansen<= o:p>

&n= bsp;

From: Hesham ElBakoury
Sent: Tuesday, August 15, 2017 8:49 PM
To: yanshen <yanshen@huawei= .com>; idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

 

Hi Yanshen,

 

In all of these use cases what problems we are trying to solve th= at have not been solved before or why existing solutions are not good enoug= h ?

 

Thanks

 

Hesham

 

From: IDNET [<= a href=3D"mailto:idnet-bounces@ietf.org">mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: id= net@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

 

Dear all,

 

Here is a summary and some inde= x (2017.08.14). Till now, whatever the case is supported or not, I tried to= organize all the content and keep the core part. It is still welcome to co= ntribute and discuss.

 

If I miss something important, = please let me know. Apologized in advance.

 

Yansen

 

 

---------  Roadmap  -= --------

***Aug. : Collecting the use ca= ses (related with NM). Rough thoughts and requirements

Sep. : Refining the cases and a= bstract the common elements

Oct. : Deeply analysis. Especia= lly on Data Format, control flow, or other key points

Nov.: F2F discussions on IETF10= 0

---------  Roadmap End &nb= sp;---------

 

 

1. Gap and Requirement Analysis=

    1.1 Network = Management requirement

    1.2 TBD=

2. Use Cases<= /p>

    2.1 Traffic = Prediction

     &= nbsp;           &nbs= p; Proposed by: yanshen@huawei.com

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

     &= nbsp;           &nbs= p; Abstract: Collect the history traffic data and external data which may i= nfluence the traffic. Predict the traffic in short/long/specific term. Avoi= d the congestion or risk in previously.

     &= nbsp;           &nbs= p;

    2.2 QoS Mana= gement

     &= nbsp;           &nbs= p; Proposed by: yanshen@huawei.com

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html<= /o:p>

     &= nbsp;           &nbs= p; Abstract: Use multiple paths to distribute the traffic flows. Adjust the= percentages. Avoid congestion and ensure QoS.

     &= nbsp;  

    2.3 App= lication (and/or DDoS) detection

     &= nbsp;           &nbs= p; Proposed by: aydinulas@gmx.net

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html<= /o:p>

     &= nbsp;           &nbs= p; Abstract: Detect the application (or attack) from network packets (HTTPS= or plain) Collect the history traffic data and identify a service or attac= k (ex: Skype, Viber, DDoS attack etc.)

     &= nbsp;  

     &= nbsp;   2.4 QoE Management

     &= nbsp;           &nbs= p; Proposed by: albert.cabellos@gmail.com

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html<= /o:p>

     &= nbsp;           &nbs= p; Abstract: Collect low-level metrics (SNR, latency, jitter, losses, etc) = and measure QoE. Then use ML to understand what is the relation between sat= isfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N = then QoE is satisfactory for the customers (please note that QoE cannot be = measured directly over your network). This is useful to understand how the = network must be operated to provide satisfactory QoE.

     &= nbsp;  

     &= nbsp;   2.5 (Encrypted) Traffic Classification<= /p>

     &= nbsp;           &nbs= p; Proposed by: jerome.francois@inria.fr; mskim16= @etri.re.kr

     &= nbsp;           &nbs= p; Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Mi= n-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html

     &= nbsp;           &nbs= p; Abstract:

     &= nbsp;           &nbs= p;          [Jerome] collect f= low-level traffic metrics such as protocol information but also meta metric= s such as distribution of packet sizes, inter-arrival times... Then use suc= h information to label the traffic with the underlying application assuming that the granularity = of classification may vary (type of application, exact application name, ve= rsion...)

     &= nbsp;           &nbs= p;          [Min-Suk Kim] cont= inuously collect packet data, then applying learning process for traffic cl= assification with generating application using deep learning models such as= CNN (convolutional neural network) and RNN (recurrent neural network). Data-set to apply into the mo= dels are generated by processing with features of information from flow in = packet data.

     &= nbsp;           &nbs= p;

     &= nbsp;   2.6 TBD

     &= nbsp;  

3. Data Focus=

    3.1 Data att= ribute

    3.2 Data for= mat

    3.3 TBD=

     &= nbsp;  

4. Support Technologies

    4.1 Benchmar= king Framework

     &= nbsp;           &nbs= p; Proposed by: pedro@nict.go.jp

     &= nbsp;           &nbs= p; Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html<= /o:p>

     &= nbsp;           &nbs= p; Abstract: A proper benchmarking framework comprises a set of reference p= rocedures, methods, and models that can (or better *must*) be followed to a= ssess the quality of an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more sp= ecific to the IDNET topics, is the inclusion, dependency, or just the gener= al relation of a standard format enforced to the data that is used (input) = and produced (output) by the framework, so a kind of "data market" can arise without requiring to transf= orm the data. The initial scope of input/output data would be the datasets,= but also the new knowledge items that are stated as a result of applying t= he benchmarking procedures defined by the framework, which can be collected together to build a database of benchmar= k results, or just contrasted with other existing entries in the database t= o know the position of the solution just evaluated. This increases the usef= ulness of IDNET.

     &= nbsp;  

    4.2 TBD=

--_000_6AE399511121AB42A34ACEF7BF25B4D29872B1DGGEMM505MBXchina_-- From nobody Wed Aug 16 21:53:15 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DA7D113266A for ; Wed, 16 Aug 2017 21:53:12 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.9 X-Spam-Level: X-Spam-Status: No, score=-1.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id A4nDaOn4saXA for ; Wed, 16 Aug 2017 21:53:10 -0700 (PDT) Received: from mail.hongo.wide.ad.jp (mail.hongo.wide.ad.jp [203.178.135.13]) by ietfa.amsl.com (Postfix) with ESMTP id 3F09B132660 for ; Wed, 16 Aug 2017 21:53:10 -0700 (PDT) Received: from [10.16.1.43] (unknown [39.110.229.193]) by mail.hongo.wide.ad.jp (Postfix) with ESMTPSA id 77C371214C02; Thu, 17 Aug 2017 13:53:03 +0900 (JST) Content-Type: text/plain; charset=us-ascii Mime-Version: 1.0 (Mac OS X Mail 10.3 \(3273\)) From: Hirochika Asai In-Reply-To: Date: Thu, 17 Aug 2017 13:53:08 +0900 Content-Transfer-Encoding: quoted-printable Message-Id: References: To: idnet@ietf.org X-Mailer: Apple Mail (2.3273) Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 17 Aug 2017 04:53:13 -0000 WIDE project also provides traffic datasets (anonymized pcap): http://mawi.wide.ad.jp/mawi/ BGP routing table archives are also publicly available for Internet = topology/BGP research: http://routeviews.org = https://www.ripe.net/analyse/internet-measurements/routing-information-ser= vice-ris/ris-raw-data Hirochika Asai > On Aug 17, 2017, at 11:09 AM, Stenio Fernandes = wrote: >=20 > http://crawdad.org > https://www.caida.org/data/ > http://imdc.datcat.org/Home >=20 >=20 > On Aug 16, 2017 22:01, "Mahesh Govind" wrote: > Hi, >=20 > Could you please give me some pointers to public dataset , which could = be used for experimenting . > Or > If any one is willing to share dataset it will be great ... >=20 > regards > mahesh >=20 >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --=20 Hirochika Asai From nobody Thu Aug 17 23:05:12 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id C21971323A1 for ; 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Thu, 17 Aug 2017 23:05:07 -0700 (PDT) MIME-Version: 1.0 Received: by 10.37.172.141 with HTTP; Thu, 17 Aug 2017 23:05:07 -0700 (PDT) In-Reply-To: References: From: Albert Cabellos Date: Fri, 18 Aug 2017 15:05:07 +0900 Message-ID: To: Hirochika Asai Cc: idnet@ietf.org Content-Type: text/plain; charset="UTF-8" Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 18 Aug 2017 06:05:11 -0000 Hi Mahesh You may also consider: http://knowledgedefinednetworking.org/ And the associated paper: https://arxiv.org/abs/1606.06222 There are data-sets that are intended to be used to learn the relation between routing, traffic and network performance (delay). In addition there are data-sets for VNF modeling. Finally, feel free to publish there any ML-related dataset, it is indeed a key aspect for ML research. Kind regards Albert On Thu, Aug 17, 2017 at 1:53 PM, Hirochika Asai wrote: > > WIDE project also provides traffic datasets (anonymized pcap): > http://mawi.wide.ad.jp/mawi/ > > BGP routing table archives are also publicly available for Internet topology/BGP research: > http://routeviews.org > https://www.ripe.net/analyse/internet-measurements/routing-information-service-ris/ris-raw-data > > Hirochika Asai > > >> On Aug 17, 2017, at 11:09 AM, Stenio Fernandes wrote: >> >> http://crawdad.org >> https://www.caida.org/data/ >> http://imdc.datcat.org/Home >> >> >> On Aug 16, 2017 22:01, "Mahesh Govind" wrote: >> Hi, >> >> Could you please give me some pointers to public dataset , which could be used for experimenting . >> Or >> If any one is willing to share dataset it will be great ... >> >> regards >> mahesh >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet > > -- > Hirochika Asai > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet From nobody Thu Aug 17 23:13:26 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E767213239E for ; Thu, 17 Aug 2017 23:13:23 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.201 X-Spam-Level: X-Spam-Status: No, score=-4.201 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 6yAgU4kYoNAh for ; Thu, 17 Aug 2017 23:13:21 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id ACE2D12EC06 for ; Thu, 17 Aug 2017 23:13:21 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v7I6DJ1s061957; Fri, 18 Aug 2017 15:13:19 +0900 (JST) Received: from mail2.nict.go.jp (mail2.nict.go.jp [133.243.18.15]) by gw2.nict.go.jp with ESMTP id v7I6DJoR061954; Fri, 18 Aug 2017 15:13:19 +0900 (JST) Received: from spectre (unknown [133.243.115.68]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail2.nict.go.jp (NICT Mail Spool Server2) with ESMTPS id 16882CD91; Fri, 18 Aug 2017 15:13:19 +0900 (JST) Date: Fri, 18 Aug 2017 15:13:18 +0900 From: Pedro Martinez-Julia To: Albert Cabellos Cc: Hirochika Asai , idnet@ietf.org Message-ID: <20170818061318.GG6804@spectre> References: MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.3 (2017-05-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 18 Aug 2017 06:13:24 -0000 Dear Albert and others, Since we have a GitHub repository to gather such information. We have a "datasets" page [1] so, please, update it with these new items. Thank you very much. Regards, Pedro [1] https://github.com/pedromj/idnet/blob/master/datasets.md -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Thu Aug 17 23:23:16 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 78C7313239E for ; Thu, 17 Aug 2017 23:23:15 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.699 X-Spam-Level: X-Spam-Status: No, score=-2.699 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id IXW3mRdYHF-e for ; Thu, 17 Aug 2017 23:23:12 -0700 (PDT) Received: from mail-yw0-x22e.google.com (mail-yw0-x22e.google.com [IPv6:2607:f8b0:4002:c05::22e]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 02C3212009C for ; Thu, 17 Aug 2017 23:23:12 -0700 (PDT) Received: by mail-yw0-x22e.google.com with SMTP id n83so25781761ywn.2 for ; Thu, 17 Aug 2017 23:23:11 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=7OQulWFk2Kzh9burjfz62JnFL0T01PaTBxQVZXX0R7c=; b=UfEKvOSQ5PLHgDsJa4EOOM9Xr+d+MfuCKV4TKty1Bi05CPNgrNr/knYzAi9cubZWKa ODbP24AzmDO1b23UY0Z8tUyGQ7ycIrS8YEza4ZPF5R2PYd8ZxNOlWFgjaEMiQqDUTAFG 2nIuGqjHqCuv7t1RkJcN8jcyE6dykCMWGtZEOPA1L6pSZ46w71eRdFH1s6fP+VmDB42v c/8h9PZVQhY2iB3qV1giGlRF4L65sEHP7d1FgyWChqrVYoyk787PWalewTf09jfZD5Ms o6hxIwm5CarcLxy2ltQ7YtNNReaQgSGYo5UkVnADoAdEzedAL6A3LHr/ij9VUmwXQlQL fA5Q== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=7OQulWFk2Kzh9burjfz62JnFL0T01PaTBxQVZXX0R7c=; b=nyDG1fl3qs0HITd95Mq5D1VGqzeovc0rsgda2suPD9gE3mvz9tQrQQgNI2xm88XIHj XiqOeoBrA0ihiT3lRJszxL/XSscq11nyLQ7Yb7Fl4+scIrWG8UNsbVzs8w9OqA4Rzrvz jYp8yUQ0FkRUSpMAQ3OsrEZkxXWk9ks7jeQJ3XS1xHldNE52ykcsrr+GGaM1KUY7X8wT Fyq9q5Crcyy7sXcOjgAS4wIq7duK60mlKCiEk5SXmD4fmHSVkwkNQoFXaGgsf4QYIdl9 5V28fvZZ7mkxaA3BHs1Dq18ukyqUFBs5mu2fYSjmypIsRjIvTDAj3Hm4DBWCBEbxTA+U 0MYg== X-Gm-Message-State: AHYfb5gVydXAU3/I5cb5sLtGpxX3TjfDA2xgedjDTg7my2JLknWRLTdx S5IysKBLT55xGFr5lPvNfLnxCKo9zw== X-Received: by 10.129.11.212 with SMTP id 203mr2253795ywl.62.1503037391120; Thu, 17 Aug 2017 23:23:11 -0700 (PDT) MIME-Version: 1.0 Received: by 10.37.172.141 with HTTP; Thu, 17 Aug 2017 23:23:10 -0700 (PDT) In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D29872B1@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D298714C@DGGEMM505-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D29872B1@DGGEMM505-MBX.china.huawei.com> From: Albert Cabellos Date: Fri, 18 Aug 2017 15:23:10 +0900 Message-ID: To: yanshen Cc: Hesham ElBakoury , "idnet@ietf.org" Content-Type: multipart/alternative; boundary="001a1142d4f8c82f700557012904" Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 18 Aug 2017 06:23:15 -0000 --001a1142d4f8c82f700557012904 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hi Hesham Complementing what Yanshen mentioned: ML (and specifically neural-nets) provide a modelling technique that work very well with complex and/or nonlinear scenarios. Traditional modeling techniques are analytical (e.g., markov chain, graph models, etc) and computational models (e.g., simulators). Analytical models do not work well with nonlinear behaviors and typically require strong assumptions about the underlying hardware. Simulator do not scale well with complexity, complex systems require long development and/or runtimes. ML work very well with nonlinear behaviors and scale very well with complexity and dimensionality. The main drawback of ML techniques is the training phase, for this you require a data-set and computational power. Once trained the neural-net is lightweight and fast. This has important advantages in optimization (QoS, VNF embedding) and prediction (traffic, etc) scenarios. Albert On Thu, Aug 17, 2017 at 1:09 PM, yanshen wrote: > Hi Hesham, > > > > Personal view. It is not important that whether the existing traffic > recognizing methods are accurate or whether the problems are serious. We > should focus on the previous and following steps. Whether we can abstract > the data or process becoming a standard format? And how to transmit the > result into the form that can be used in the following configuring proces= s. > > > > Yansen > > > > *From:* Hesham ElBakoury > *Sent:* Thursday, August 17, 2017 5:20 AM > *To:* yanshen > > *Cc:* idnet@ietf.org > *Subject:* RE: Summary 20170814 & IDN dedicated session call for case > > > > Hi Yansen, > > > > Thanks for your detailed response! > > > > There are also existing solutions which use AI/ML in (some of) these use > cases. How our AI solutions will be better than existing AI solutions. > > Let us take classification of encrypted traffic as an example. There are > already different solutions that use AI/ML to classify the encrypted > traffic. > > What are the problems and shortcomings of these solutions that should be > addressed in new and better solutions which also use AI/ML ? > > > > Hesham > > > > *From:* yanshen > *Sent:* Wednesday, August 16, 2017 7:26 AM > *To:* Hesham ElBakoury > *Cc:* idnet@ietf.org > *Subject:* RE: Summary 20170814 & IDN dedicated session call for case > > > > Hi Hesham, > > > > I think this question seems simple but really significant. Please forgive > if the answer was redundant. And it is welcome to point out my lacks if y= ou > have some different views. > > > > In general, the new method may bring potential benefits. There had been > some discussions before in our mail list. Please search the keyword > =E2=80=9Cbenefit=E2=80=9D and check the result. But what I want to descri= be here are the > following several points. > > > > Firstly, in essence, the AI method can solve the scalability problem in > each use case but existing methods cannot. In other words, the existing > methods can hardly cover the complexity along with the huge increment of > service in the future by repeating the current solutions multi-times. Fo= r > example, if the cost (including time and economic) of configuring a VPN i= s > C, so that it will cost at least 100*C for 100 VPNs (this is ideal, it is > even worse in practice). Even though somebody may say the software progra= ms > can also provide the automatic solution for this problems, but the > over-linear incremental complexity will easily breakthrough the upper cos= t > limit of that we can pay. However, since the AI method may solve the sam= e > question in yet another new way so that it may change the complexity from > over-linear growth to logarithmic growth (or even lower). This is > significant because the current methods and their improved version can > hardly implement such a change. I think this is one of the core problems > that AI solves. It builds up a new pattern that can bear the pressure fro= m > new services and new requirements, which the traditional method cannot > support. > > > > Secondly, the new method can increase the automation level of the network > but not only provide an automatic program. The AI method can make more > process automatic such as the decision and policy deploying. Some of them > cannot be implemented by program. For example, if we plan to capture and > modify a series of devices/networks configuration, how can we implement > that? The devices may be from different venders. The topology may be > different. Their interfaces may be different. This situation is hard to > solve by =E2=80=9Cif-else-then=E2=80=9D logic or very hard. However, if w= e can abstract the > configuration and the policies with some unified expression, such as usin= g > standard 0-1 series or formatted packet, the consulting between various > devices will be simplified a lot. Of course, the =E2=80=9Cif-else-then=E2= =80=9D logic can > implement that but the core of problem is the complexity. > > > > However, some of the cases should not be mentioned. The aim of IDNet is > introducing AI to serve the Network but not Application. Therefore, any > case, whose output implements only a new function or new application, may > be not suitable (personal view). Because it might be hardly produced by > existing method but it is only a new tool but does not serve the network. > We should focus on the case, whose output produces a decision, a strategy= , > or a configuration relative with network/device, or the cases that direct= ly > serve the Network Management, especially improving the efficiency and > decreasing the cost. They are valuable. So the answer may not depend on > whether the existing methods can or cannot, but depends on the AI can or > cannot provide a new form. > > > > BTW, the new AI method may perform =E2=80=9Cbetter=E2=80=9D than existing= solutions. But > in my opinion, this =E2=80=9Cbetter=E2=80=9D cannot become the sufficient= condition to > apply because it is often covered by the cost of deployment. > > > > In a word, in any cases, the IDNet will provide a scalability solutions > and increase the automation level that the existing method and automatic > software programs cannot. And our work here is to explore the > standardization points and define the process, the data format, the > architecture and etc., which support the compatibility for the various > function entities. So that make the AI method enlarge the power of the > network. > > > > > > Regards, > > > > Yansen > > > > *From:* Hesham ElBakoury > *Sent:* Tuesday, August 15, 2017 8:49 PM > *To:* yanshen ; idnet@ietf.org > *Subject:* RE: Summary 20170814 & IDN dedicated session call for case > > > > Hi Yanshen, > > > > In all of these use cases what problems we are trying to solve that have > not been solved before or why existing solutions are not good enough ? > > > > Thanks > > > > Hesham > > > > *From:* IDNET [mailto:idnet-bounces@ietf.org ] *O= n > Behalf Of *yanshen > *Sent:* Sunday, August 13, 2017 8:36 PM > *To:* idnet@ietf.org > *Subject:* [Idnet] Summary 20170814 & IDN dedicated session call for case > > > > Dear all, > > > > Here is a summary and some index (2017.08.14). Till now, whatever the cas= e > is supported or not, I tried to organize all the content and keep the cor= e > part. It is still welcome to contribute and discuss. > > > > If I miss something important, please let me know. Apologized in advance. > > > > Yansen > > > > > > --------- Roadmap --------- > > ***Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > > Sep. : Refining the cases and abstract the common elements > > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > > Nov.: F2F discussions on IETF100 > > --------- Roadmap End --------- > > > > > > 1. Gap and Requirement Analysis > > 1.1 Network Management requirement > > 1.2 TBD > > 2. Use Cases > > 2.1 Traffic Prediction > > Proposed by: yanshen@huawei.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00131.html > > Abstract: Collect the history traffic data and externa= l > data which may influence the traffic. Predict the traffic in > short/long/specific term. Avoid the congestion or risk in previously. > > > > 2.2 QoS Management > > Proposed by: yanshen@huawei.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00131.html > > Abstract: Use multiple paths to distribute the traffic > flows. Adjust the percentages. Avoid congestion and ensure QoS. > > > > 2.3 Application (and/or DDoS) detection > > Proposed by: aydinulas@gmx.net > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00133.html > > Abstract: Detect the application (or attack) from > network packets (HTTPS or plain) Collect the history traffic data and > identify a service or attack (ex: Skype, Viber, DDoS attack etc.) > > > > 2.4 QoE Management > > Proposed by: albert.cabellos@gmail.com > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00137.html > > Abstract: Collect low-level metrics (SNR, latency, > jitter, losses, etc) and measure QoE. Then use ML to understand what is t= he > relation between satisfactory QoE and the low-level metrics. As an exampl= e > learn that when delay>N then QoE is degraded, but when M is satisfactory for the customers (please note that QoE cannot be measure= d > directly over your network). This is useful to understand how the network > must be operated to provide satisfactory QoE. > > > > 2.5 (Encrypted) Traffic Classification > > Proposed by: jerome.francois@inria.fr; > mskim16@etri.re.kr > > Track: [Jerome] https://www.ietf.org/mail- > archive/web/idnet/current/msg00141.html ; [Min-Suk Kim] > https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html > > Abstract: > > [Jerome] collect flow-level traffic metrics > such as protocol information but also meta metrics such as distribution o= f > packet sizes, inter-arrival times... Then use such information to label t= he > traffic with the underlying application assuming that the granularity of > classification may vary (type of application, exact application name, > version...) > > [Min-Suk Kim] continuously collect packet > data, then applying learning process for traffic classification with > generating application using deep learning models such as CNN > (convolutional neural network) and RNN (recurrent neural network). Data-s= et > to apply into the models are generated by processing with features of > information from flow in packet data. > > > > 2.6 TBD > > > > 3. Data Focus > > 3.1 Data attribute > > 3.2 Data format > > 3.3 TBD > > > > 4. Support Technologies > > 4.1 Benchmarking Framework > > Proposed by: pedro@nict.go.jp > > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00146.html > > Abstract: A proper benchmarking framework comprises a > set of reference procedures, methods, and models that can (or better > *must*) be followed to assess the quality of an AI mechanism proposed to = be > applied to the network management/control area. Moreover, and much more > specific to the IDNET topics, is the inclusion, dependency, or just the > general relation of a standard format enforced to the data that is used > (input) and produced (output) by the framework, so a kind of "data market= " > can arise without requiring to transform the data. The initial scope of > input/output data would be the datasets, but also the new knowledge items > that are stated as a result of applying the benchmarking procedures defin= ed > by the framework, which can be collected together to build a database of > benchmark results, or just contrasted with other existing entries in the > database to know the position of the solution just evaluated. This > increases the usefulness of IDNET. > > > > 4.2 TBD > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --001a1142d4f8c82f700557012904 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi Hesham

Complementing what Yanshen me= ntioned:

ML (and specifically neural-nets) provide= a modelling technique that work very well with complex and/or nonlinear sc= enarios.

Traditional modeling techniques are analy= tical (e.g., markov chain, graph models, etc) and computational models (e.g= ., simulators).

Analytical models do not work well with nonli= near behaviors and typically require strong assumptions about the underlyin= g hardware.

Simulator do not scale well with compl= exity, complex systems require long development and/or runtimes.

ML work very well with nonlinear behaviors and scale very = well with complexity and dimensionality. The main drawback of ML techniques= is the training phase, for this you require a data-set and computational p= ower. Once trained the neural-net is lightweight and fast.

This has important advantages in optimization (QoS, V= NF embedding) and prediction (traffic, etc) scenarios.

=
Albert

On Thu, Aug 17, 2017 at 1:09 PM, yanshen <yanshen@huawei.com>= ; wrote:

Hi Hesh= am,

= =C2=A0

Persona= l view. It is not important that whether the existing traffic recognizing m= ethods are accurate or whether the problems are serious. We should focus on= the previous and following steps. Whether we can abstract the data or process becoming a standard format? And how to= transmit the result into the form that can be used in the following config= uring process.

= =C2=A0

Yansen<= u>

= =C2=A0

From: Hesham ElBakoury
Sent: Thursday, August 17, 2017 5:20 AM
To: yanshen <yanshen@huawei.com>


Cc: idnet@ietf.o= rg
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

=C2=A0

Hi Yansen,

=C2=A0

Thanks for your detailed response!

=C2=A0

There are also existing solutions which use AI/ML in (some of) th= ese use cases. How our AI solutions will be better than existing AI solutio= ns.

Let us take classification of encrypted traffic as an example. Th= ere are already different solutions that use AI/ML to classify the encrypte= d traffic.

What are the problems and shortcomings of these solutions =C2=A0t= hat should be addressed in new and better solutions which also use AI/ML ?<= u>

=C2=A0

Hesham

=C2=A0

From: yanshen
Sent: Wednesday, August 16, 2017 7:26 AM
To: Hesham ElBakoury
Cc: idnet@ietf.o= rg
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

=C2=A0

Hi Hesh= am,

= =C2=A0

I think= this question seems simple but really significant. Please forgive if the a= nswer was redundant. And it is welcome to point out my lacks if you have so= me different views.

= =C2=A0

In gene= ral, the new method may bring potential benefits. There had been some discu= ssions before in our mail list. Please search the keyword =E2=80=9Cbenefit= =E2=80=9D and check the result. But what I want to describe here are the following several points.

= =C2=A0

Firstly= , in essence, the AI method can solve the scalability problem in each use c= ase but existing methods cannot. In other words, the existing methods can h= ardly cover the complexity along with the huge increment of service in the future by repeating the current solut= ions multi-times.=C2=A0 For example, if the cost (including time and econom= ic) of configuring a VPN is C, so that it will cost at least 100*C for 100 = VPNs (this is ideal, it is even worse in practice). Even though somebody may say the software programs can also = provide the automatic solution for this problems, but the over-linear incre= mental complexity will easily breakthrough the upper cost limit of=C2=A0 th= at we can pay. However, since the AI method may solve the same question in yet another new way so that it may c= hange the complexity from over-linear growth to logarithmic growth (or even= lower). This is significant because the current methods and their improved= version can hardly implement such a change. I think this is one of the core problems that AI solves. It buil= ds up a new pattern that can bear the pressure from new services and new re= quirements, which the traditional method cannot support.

= =C2=A0

Secondl= y, the new method can increase the automation level of the network but not = only provide an automatic program. The AI method can make more process auto= matic such as the decision and policy deploying. Some of them cannot be implemented by program. For example, if = we plan to capture and modify a series of devices/networks configuration, h= ow can we implement that? The devices may be from different venders. The to= pology may be different. Their interfaces may be different. This situation is hard to solve by =E2=80=9Cif-else-then= =E2=80=9D logic or very hard. However, if we can abstract the configuration= and the policies with some unified expression, such as using standard 0-1 = series or formatted packet, the consulting between various devices will be simplified a lot. Of course, the =E2=80=9Cif-else-= then=E2=80=9D logic can implement that but the core of problem is the compl= exity.

= =C2=A0

However= , some of the cases should not be mentioned. The aim of IDNet is introducin= g AI to serve the Network but not Application. Therefore, any case, whose o= utput implements only a new function or new application, may be not suitable (personal view). Because it might be = hardly produced by existing method but it is only a new tool but does not s= erve the network. We should focus on the case, whose output produces a deci= sion, a strategy, or a configuration relative with network/device, or the cases that directly serve the Network= Management, especially improving the efficiency and decreasing the cost. T= hey are valuable.=C2=A0 So the answer may not depend on whether the existin= g methods can or cannot, but depends on the AI can or cannot provide a new form.

= =C2=A0

BTW, th= e new AI method may perform =E2=80=9Cbetter=E2=80=9D than existing solution= s. But in my opinion, this =E2=80=9Cbetter=E2=80=9D cannot become the suffi= cient condition to apply because it is often covered by the cost of deploym= ent.=C2=A0

= =C2=A0

In a wo= rd, in any cases, the IDNet will provide a scalability solutions and increa= se the automation level that the existing method and automatic software pro= grams cannot. And our work here is to explore the standardization points and define the process, the data format= , the architecture and etc., which support the compatibility for the variou= s function entities. So that make the AI method enlarge the power of the ne= twork.

= =C2=A0

= =C2=A0

Regards= ,

= =C2=A0

Yansen<= u>

= =C2=A0

From: Hesham ElBakoury
Sent: Tuesday, August 15, 2017 8:49 PM
To: yanshen <yanshen@huawei.com>; idnet@ietf.org
Subject: RE: Summary 20170814 & IDN dedicated session call for c= ase

=C2=A0

Hi Yanshen,

=C2=A0

In all of these use cases what problems we are trying to solve th= at have not been solved before or why existing solutions are not good enoug= h ?

=C2=A0

Thanks

=C2=A0

Hesham

=C2=A0

From: IDNET [<= a href=3D"mailto:idnet-bounces@ietf.org" target=3D"_blank">mailto:idnet-bou= nces@ietf.org] On Behalf Of yanshen
Sent: Sunday, August 13, 2017 8:36 PM
To: idnet@ietf.org
Subject: [Idnet] Summary 20170814 & IDN dedicated session call f= or case

=C2=A0

Dear all, =

=C2=A0

Here is a summary and some inde= x (2017.08.14). Till now, whatever the case is supported or not, I tried to= organize all the content and keep the core part. It is still welcome to co= ntribute and discuss.

=C2=A0

If I miss something important, = please let me know. Apologized in advance.

=C2=A0

Yansen

=C2=A0

=C2=A0

--------- =C2=A0Roadmap =C2=A0-= --------

***Aug. : Collecting the use ca= ses (related with NM). Rough thoughts and requirements=

Sep. : Refining the cases and a= bstract the common elements

Oct. : Deeply analysis. Especia= lly on Data Format, control flow, or other key points<= /p>

Nov.: F2F discussions on IETF10= 0

--------- =C2=A0Roadmap End =C2= =A0---------

=C2=A0

=C2=A0

1. Gap and Requirement Analysis=

=C2=A0=C2=A0=C2=A0 1.1 Network = Management requirement

=C2=A0=C2=A0=C2=A0 1.2 TBD

2. Use Cases

=C2=A0=C2=A0=C2=A0 2.1 Traffic = Prediction

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Proposed by: yanshen@huawei.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Abstract: Collect the history traffic data and external data which may = influence the traffic. Predict the traffic in short/long/specific term. Avo= id the congestion or risk in previously.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0

=C2=A0=C2=A0=C2=A0 2.2 QoS Mana= gement

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Proposed by: yanshen@huawei.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Abstract: Use multiple paths to distribute the traffic flows. Adjust th= e percentages. Avoid congestion and ensure QoS.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0=C2=A02.3 App= lication (and/or DDoS) detection

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Proposed by: aydinulas@gmx.net

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Abstract: Detect the application (or attack) from network packets (HTTP= S or plain) Collect the history traffic data and identify a service or atta= ck (ex: Skype, Viber, DDoS attack etc.)

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0 2.4 QoE Management

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Proposed by: albert.cabellos@gmail.com

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Abstract: Collect low-level metrics (SNR, latency, jitter, losses, etc)= and measure QoE. Then use ML to understand what is the relation between sa= tisfactory QoE and the low-level metrics. As an example learn that when delay>N then QoE is degraded, but when M<delay<N = then QoE is satisfactory for the customers (please note that QoE cannot be = measured directly over your network). This is useful to understand how the = network must be operated to provide satisfactory QoE.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0 2.5 (Encrypted) Traffic Classification

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Proposed by: jerome.francois@inria.fr; mskim16@etri.re.kr

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html= ; [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/curre= nt/msg00153.html

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Abstract:

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 [Jerome] collect = flow-level traffic metrics such as protocol information but also meta metri= cs such as distribution of packet sizes, inter-arrival times... Then use su= ch information to label the traffic with the underlying application assuming that the granularity = of classification may vary (type of application, exact application name, ve= rsion...)

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 [Min-Suk Kim] con= tinuously collect packet data, then applying learning process for traffic c= lassification with generating application using deep learning models such a= s CNN (convolutional neural network) and RNN (recurrent neural network). Data-set to apply into the mo= dels are generated by processing with features of information from flow in = packet data.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0 2.6 TBD

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0

3. Data Focus

=C2=A0=C2=A0=C2=A0 3.1 Data att= ribute

=C2=A0=C2=A0=C2=A0 3.2 Data for= mat

=C2=A0=C2=A0=C2=A0 3.3 TBD

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0

4. Support Technologies<= u>

=C2=A0=C2=A0=C2=A0 4.1 Benchmar= king Framework

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Proposed by: pedro@nict.go.jp

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html=

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0 Abstract: A proper benchmarking framework comprises a set of reference = procedures, methods, and models that can (or better *must*) be followed to = assess the quality of an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more sp= ecific to the IDNET topics, is the inclusion, dependency, or just the gener= al relation of a standard format enforced to the data that is used (input) = and produced (output) by the framework, so a kind of "data market" can arise without requiring to transf= orm the data. The initial scope of input/output data would be the datasets,= but also the new knowledge items that are stated as a result of applying t= he benchmarking procedures defined by the framework, which can be collected together to build a database of benchmar= k results, or just contrasted with other existing entries in the database t= o know the position of the solution just evaluated. This increases the usef= ulness of IDNET.

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0

=C2=A0=C2=A0=C2=A0 4.2 TBD


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--001a1142d4f8c82f700557012904-- From nobody Fri Aug 18 10:15:05 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 7238B1320B5 for ; Fri, 18 Aug 2017 10:15:04 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.399 X-Spam-Level: X-Spam-Status: No, score=-2.399 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, FREEMAIL_FORGED_FROMDOMAIN=0.199, FREEMAIL_FROM=0.001, HEADER_FROM_DIFFERENT_DOMAINS=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id uq9WVwKIpgwm for ; Fri, 18 Aug 2017 10:14:59 -0700 (PDT) Received: from mail-yw0-x22c.google.com (mail-yw0-x22c.google.com [IPv6:2607:f8b0:4002:c05::22c]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 56965120721 for ; Fri, 18 Aug 2017 10:14:59 -0700 (PDT) Received: by mail-yw0-x22c.google.com with SMTP id p68so62614696ywg.0 for ; Fri, 18 Aug 2017 10:14:59 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:sender:in-reply-to:references:from:date:message-id :subject:to:cc; bh=E5+KR5TwsF+pDiMFTtxSLDV4AXeI+iQZiaXcVlD1PlQ=; b=FfRr7XhZ6JfI3yWcX9KdXs8WPTp5OzEHmOE9w9Y/M+sLrx8cDcB8c7Nsa4XtA+UKRs Rbwhnd5yYpoOpHkHLY2aR7wequP61x/9fFWtX4sTwSXRJXoOQzNxHRgFuk6D0V6XhF3q 1X94ZDsz1y7g97jStPUc8xNRyvTNhhjkZsV4H9lxNzIVHNe9PjNlcR1UKG+q/Se/g/7z GWCGUQK5rKL28JY5vN+65OTkUJZ8gLd05XGsq7d68QGt1esRPBa5HyzS415mMYeldg4D DVIPPFrrZtOMPiT6TLcfTbfMreSDP0JQcBu0vztEVamyiXo/7KAy3I/HA7ArI6SvQIAr N3nQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:sender:in-reply-to:references:from :date:message-id:subject:to:cc; bh=E5+KR5TwsF+pDiMFTtxSLDV4AXeI+iQZiaXcVlD1PlQ=; b=oB2a6XnRo+1itxk9xmlTV2ZZEB1WDlIii/SidtCXJx/7BxH2TyYuNEaERwtqfIxFjd x/zfvaMCkUi0UJVFgme6yEfWH+ETJA+xmkHAQTGLFDWz1XdiN5CE5a5EiDMPAU373WIB QQ7FJ08yUqxKplPwsgNJbnNPbGNNzp1NewrwleOPwTDSLAtIpYoOAmMTz+2GIHepOMyg qHHvRDYvcQ7dS2p1aZKgqT5RQJXkGMDCYR9mK3XwZgvdtb5SkleANIjSm4MwxjmiCU62 XHDtLlMMR1O6A7zF3juWQHrMSo9L3erHo0aTH2HiIUiGwSvaSHHgAbWLYwIv2ZM/oi94 veUA== X-Gm-Message-State: AHYfb5gg0gbxQROZ+n8H+wEIIGtswiEGu7WIQTt48QuBmOs2U1pHoZ5t kNeQUPiC/RqreYGXds1xf5G6q8Qv1BcnGr0= X-Received: by 10.37.110.134 with SMTP id j128mr7893778ybc.280.1503076498377; Fri, 18 Aug 2017 10:14:58 -0700 (PDT) MIME-Version: 1.0 Sender: steniofernandes@gmail.com Received: by 10.37.104.74 with HTTP; Fri, 18 Aug 2017 10:14:17 -0700 (PDT) In-Reply-To: <6AE399511121AB42A34ACEF7BF25B4D298461D@DGGEMM505-MBX.china.huawei.com> References: <6AE399511121AB42A34ACEF7BF25B4D2982F34@DGGEMM505-MBX.china.huawei.com> <78A2745BE9B57D4F9D27F86655EB87F92599490E@SJCEML701-CHM.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D298461D@DGGEMM505-MBX.china.huawei.com> From: Stenio Fernandes Date: Fri, 18 Aug 2017 13:14:17 -0400 X-Google-Sender-Auth: k80GuvjslS-wfHOMdFJ9RAmL41c Message-ID: To: yanshen Cc: Haoyu song , "idnet@ietf.org" Content-Type: multipart/alternative; boundary="001a1148b9c8c192c805570a44c6" Archived-At: Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for case X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 18 Aug 2017 17:15:04 -0000 --001a1148b9c8c192c805570a44c6 Content-Type: text/plain; charset="UTF-8" Hi Yansen et al. Here is my take on the anomaly detection use case (2.6). I know that I was supposed to write just 'rough thoughts and requirements', but I decided to write in advance regarding the upcoming tasks (sep/oct). ====================================================================== Definitions: First, it is important to understand that the scientific literature allows different names for anomaly detection, such as outlier detection, novelty detection, noise detection, deviation detection, or mining exception. This not so strict naming also brings a number of definitions for what could be considered as an outlier or anomaly. A more general definition for an outlier is an observation that is somewhat inconsistent when compared to the remainder of the set of observations. In the specific case of a network anomaly, such observation may cause network failures and performance problems such as congestions and denial of services (DOS). Anomalies can be generally classified into three categories, namely point, contextual, and collective. Point anomaly is related to a single observation whereas collective anomaly is related to a series of observations. For instance, a SYN Flood attack can be considered a collective anomaly since a single TCP SYN segment is valid and is not considered a point anomaly. Contextual anomaly is the interpretation whether the point or collective anomaly is, in fact, an anomaly given a proper context. Applications: Abnormal behavior from packets or streams of packets (flows) requires precise and, in some cases, quick detection so that the network could react and take appropriate measures to mitigate its short and long term effects. Ideally, the network must be intelligent enough to automatically learn what is normal traffic so it could adapt to any abnormal traffic patterns, including zero-day attacks. Of course, not all anomalies come from intentional attacks. Abnormal behavior could also come from misconfiguration or malfunctions in the network. Data / Features: Most techniques for anomaly detection use features extracted from transport or network layer data, mainly due to the widespread adoption of IPFIX/NetFlow on routers/switches. It is possible to create new variables (a.k.a. Feature Engineering in the data mining/machine learning lingo) from the raw data, such as packet or flow inter-arrival times. Depending on the measurement process one could also include data from other Internet layers to make the detection more accurate. One must be aware that scalability is always a concern when dealing with massive amount of data. The types and characteristics of the input data often limit the choices of techniques that could be used for anomaly detection. Some techniques require labeled data (i.e., using prior knowledge to identify an observation as normal or abnormal), which in most cases requires enormous processing efforts. Techniques: Methods for anomaly detection come from several fields and their subdisciplines, such as Statistics, Machine Learning, Data Mining, Information Theory, Spectral Theory, and the like. Therefore, there are a number of techniques to handle (i.e., detection and/or removal) abnormal observations. As the main scope and interest of IDNET are on techniques that can learn from the incoming network traffic and events, the behavioral-based anomaly detection ones seem the best fit. Behavioral-based anomaly detection methods are usually classified as supervised, semi-supervised, or unsupervised, depending on the availability of labeled data for the training phase. Supervised and semi-supervised learning require labeled data whereas unsupervised learning is able to work with unlabeled data. Unsupervised anomaly detection techniques create initially a region (e.g., a cluster in an n-dimensional hyperspace) that represents the limits of a normal behavior so that any observation beyond those bounds is considered an anomaly. They can easily (automatically) adapt to changes in the incoming network traffic/events. As far as we are concerned to recent systems for anomaly detection, there is a clear trending on hybrid techniques (i.e., the combination of two or more techniques) to overcome well-known limitations of each individual class of techniques, such as low precision/recall, high processing overhead, and the like. This means in general building a system with two or more phases that combines supervised, semi-supervised, and unsupervised learning in sequence. The outputs of anomaly detection techniques can be scores and/or labels. Of course, the objectives of the classification problem (i.e., either single class or multiclass) define their outputs. Therefore, given the type of classification problem, a number of methods can be applied, such as the ones based on unsupervised clustering. Challenges: Current challenges for network anomaly detection includes i) dealing with high dimensional data, class imbalance, and noise, ii) performing fast and accurate feature engineering, iii) ensuring cluster homogeneity, iv) lowering false alarm rate, and v) handling sequential, spatial, and graph data simultaneously. ====================================================================== Cheers, Stenio On Mon, Aug 14, 2017 at 10:37 PM, yanshen wrote: > Hi Haoyu, > > Agree. These two crucial cases in Network Management are what we are > focusing on now. Since we plan to organize a dedicated session in NMRG, all > the discussion will converge to the area of Network Management before Nov. > > Just expect Stenio's output few days later : ) > > Yansen > > > > -----Original Message----- > > From: steniofernandes@gmail.com [mailto:steniofernandes@gmail.com] On > > Behalf Of Stenio Fernandes > > Sent: Tuesday, August 15, 2017 2:09 AM > > To: Haoyu song > > Cc: yanshen ; idnet@ietf.org > > Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call for > case > > > > Haoyu, > > > > I'm working on the anomaly detection use case and will send it to the > list this > > week. > > > > Stenio > > > > On Mon, Aug 14, 2017 at 12:47 PM, Haoyu song > > wrote: > > > Yansen, > > > > > > > > > > > > I see two key use cases are missing in the current list: root cause > > > analysis and anomaly detection. Those two are likely to use ML-based > > > solutions and the first one has already received a lot of research. > > > > > > > > > > > > Haoyu > > > > > > > > > > > > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen > > > Sent: Sunday, August 13, 2017 8:36 PM > > > To: idnet@ietf.org > > > Subject: [Idnet] Summary 20170814 & IDN dedicated session call for > > > case > > > > > > > > > > > > Dear all, > > > > > > > > > > > > Here is a summary and some index (2017.08.14). Till now, whatever the > > > case is supported or not, I tried to organize all the content and keep > > > the core part. It is still welcome to contribute and discuss. > > > > > > > > > > > > If I miss something important, please let me know. Apologized in > advance. > > > > > > > > > > > > Yansen > > > > > > > > > > > > > > > > > > --------- Roadmap --------- > > > > > > ***Aug. : Collecting the use cases (related with NM). Rough thoughts > > > and requirements > > > > > > Sep. : Refining the cases and abstract the common elements > > > > > > Oct. : Deeply analysis. Especially on Data Format, control flow, or > > > other key points > > > > > > Nov.: F2F discussions on IETF100 > > > > > > --------- Roadmap End --------- > > > > > > > > > > > > > > > > > > 1. Gap and Requirement Analysis > > > > > > 1.1 Network Management requirement > > > > > > 1.2 TBD > > > > > > 2. Use Cases > > > > > > 2.1 Traffic Prediction > > > > > > Proposed by: yanshen@huawei.com > > > > > > Track: > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html > > > > > > Abstract: Collect the history traffic data and > > > external data which may influence the traffic. Predict the traffic in > > > short/long/specific term. Avoid the congestion or risk in previously. > > > > > > > > > > > > 2.2 QoS Management > > > > > > Proposed by: yanshen@huawei.com > > > > > > Track: > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html > > > > > > Abstract: Use multiple paths to distribute the > > > traffic flows. Adjust the percentages. Avoid congestion and ensure QoS. > > > > > > > > > > > > 2.3 Application (and/or DDoS) detection > > > > > > Proposed by: aydinulas@gmx.net > > > > > > Track: > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html > > > > > > Abstract: Detect the application (or attack) from > > > network packets (HTTPS or plain) Collect the history traffic data and > > > identify a service or attack (ex: Skype, Viber, DDoS attack etc.) > > > > > > > > > > > > 2.4 QoE Management > > > > > > Proposed by: albert.cabellos@gmail.com > > > > > > Track: > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html > > > > > > Abstract: Collect low-level metrics (SNR, latency, > > > jitter, losses, etc) and measure QoE. Then use ML to understand what > > > is the relation between satisfactory QoE and the low-level metrics. As > > > an example learn that when delay>N then QoE is degraded, but when > > > M > > QoE cannot be measured directly over your network). This is useful to > > > understand how the network must be operated to provide satisfactory > QoE. > > > > > > > > > > > > 2.5 (Encrypted) Traffic Classification > > > > > > Proposed by: jerome.francois@inria.fr; > > > mskim16@etri.re.kr > > > > > > Track: [Jerome] > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; > > > [Min-Suk Kim] > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html > > > > > > Abstract: > > > > > > [Jerome] collect flow-level traffic > > > metrics such as protocol information but also meta metrics such as > > > distribution of packet sizes, inter-arrival times... Then use such > > > information to label the traffic with the underlying application > > > assuming that the granularity of classification may vary (type of > > > application, exact application name, > > > version...) > > > > > > [Min-Suk Kim] continuously collect packet > > > data, then applying learning process for traffic classification with > > > generating application using deep learning models such as CNN > > > (convolutional neural > > > network) and RNN (recurrent neural network). Data-set to apply into > > > the models are generated by processing with features of information > > > from flow in packet data. > > > > > > > > > > > > 2.6 TBD > > > > > > > > > > > > 3. Data Focus > > > > > > 3.1 Data attribute > > > > > > 3.2 Data format > > > > > > 3.3 TBD > > > > > > > > > > > > 4. Support Technologies > > > > > > 4.1 Benchmarking Framework > > > > > > Proposed by: pedro@nict.go.jp > > > > > > Track: > > > https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html > > > > > > Abstract: A proper benchmarking framework > > comprises > > > a set of reference procedures, methods, and models that can (or better > > > *must*) be followed to assess the quality of an AI mechanism proposed > > > to be applied to the network management/control area. Moreover, and > > > much more specific to the IDNET topics, is the inclusion, dependency, > > > or just the general relation of a standard format enforced to the data > > > that is used (input) and produced > > > (output) by the framework, so a kind of "data market" can arise > > > without requiring to transform the data. The initial scope of > > > input/output data would be the datasets, but also the new knowledge > > > items that are stated as a result of applying the benchmarking > > > procedures defined by the framework, which can be collected together > > > to build a database of benchmark results, or just contrasted with > > > other existing entries in the database to know the position of the > > > solution just evaluated. This increases the usefulness of IDNET. > > > > > > > > > > > > 4.2 TBD > > > > > > > > > _______________________________________________ > > > IDNET mailing list > > > IDNET@ietf.org > > > https://www.ietf.org/mailman/listinfo/idnet > > > > > > > > > > > -- > > Prof. Stenio Fernandes > > CIn/UFPE > > http://www.steniofernandes.com > -- Prof. Stenio Fernandes CIn/UFPE http://www.steniofernandes.com --001a1148b9c8c192c805570a44c6 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi Yansen et al.

Here is my take on the= anomaly detection use case (2.6). I know that I was supposed to write just= 'rough thoughts and requirements', but I decided to write in advan= ce regarding the upcoming tasks (sep/oct).=C2=A0

= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

=
Definitions:

First, it is important to understand that the sci= entific literature allows different names for anomaly detection, such as ou= tlier detection, novelty detection, noise detection, deviation detection, o= r mining exception. This not so strict naming also brings a number of defin= itions for what could be considered as an outlier or anomaly. A more genera= l definition for an outlier is an observation that is somewhat inconsistent= when compared to the remainder of the set of observations. In the specific= case of a network anomaly, such observation may cause network failures and= performance problems such as congestions and denial of services (DOS).
=
Anomalies can be generally classified into three categories, namely poi= nt, contextual, and collective. Point anomaly is related to a single observ= ation whereas collective anomaly is related to a series of observations. Fo= r instance, a SYN Flood attack can be considered a collective anomaly since= a single TCP SYN segment is valid and is not considered a point anomaly. C= ontextual anomaly is the interpretation whether the point or collective ano= maly is, in fact, an anomaly given a proper context.

Applications:
Abnormal behavior from packets or streams of packets (flows) requires= precise and, in some cases, quick detection so that the network could reac= t and take appropriate measures to mitigate its short and long term effects= . Ideally, the network must be intelligent enough to automatically learn wh= at is normal traffic so it could adapt to any abnormal traffic patterns, in= cluding zero-day attacks. Of course, not all anomalies come from intentiona= l attacks. Abnormal behavior could also come from misconfiguration or malfu= nctions in the network.

Data / Features:

Most techniques for = anomaly detection use features extracted from transport or network layer da= ta, mainly due to the widespread adoption of IPFIX/NetFlow on routers/switc= hes. It is possible to create new variables (a.k.a. Feature Engineering in = the data mining/machine learning lingo) from the raw data, such as packet o= r flow inter-arrival times. Depending on the measurement process one could = also include data from other Internet layers to make the detection more acc= urate. One must be aware that scalability is always a concern when dealing = with massive amount of data.

The types and characteristics of the in= put data often limit the choices of techniques that could be used for anoma= ly detection. Some techniques require labeled data (i.e., using prior knowl= edge to identify an observation as normal or abnormal), which in most cases= requires enormous processing efforts.

Techniques:

Methods fo= r anomaly detection come from several fields and their subdisciplines, such= as Statistics, Machine Learning, Data Mining, Information Theory, Spectral= Theory, and the like. Therefore, there are a number of techniques to handl= e (i.e., detection and/or removal) abnormal observations. As the main scope= and interest of IDNET are on techniques that can learn from the incoming n= etwork traffic and events, the behavioral-based anomaly detection ones seem= the best fit. =C2=A0

Behavioral-based anomaly detection methods are= usually classified as supervised, semi-supervised, or unsupervised, depend= ing on the availability of labeled data for the training phase. Supervised = and semi-supervised learning require labeled data whereas unsupervised lear= ning is able to work with unlabeled data.

Unsupervised anomaly detec= tion techniques create initially a region (e.g., a cluster in an n-dimensio= nal hyperspace) that represents the limits of a normal behavior so that any= observation beyond those bounds is considered an anomaly. They can easily = (automatically) adapt to changes in the incoming network traffic/events.
As far as we are concerned to recent systems for anomaly detection, th= ere is a clear trending on hybrid techniques (i.e., the combination of two = or more techniques) to overcome well-known limitations of each individual c= lass of techniques, such as low precision/recall, high processing overhead,= and the like. This means in general building a system with two or more pha= ses that combines supervised, semi-supervised, and unsupervised learning in= sequence. =C2=A0

The outputs of anomaly detection techniques can be= scores and/or labels. Of course, the objectives of the classification prob= lem (i.e., either single class or multiclass) define their outputs. Therefo= re, given the type of classification problem, a number of methods can be ap= plied, such as the ones based on unsupervised clustering. =C2=A0

Cha= llenges:

Current challenges for network anomaly detection includes i= ) dealing with high dimensional data, class imbalance, and noise, ii) perfo= rming fast and accurate feature engineering, iii) ensuring cluster homogene= ity, iv) lowering false alarm rate, and v) handling sequential, spatial, an= d graph data simultaneously.

=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

Cheers,

Stenio

On Mon, Aug 14, 2017 at 10:37 PM, yanshen <yanshe= n@huawei.com> wrote:
Hi Hao= yu,

Agree. These two crucial cases in Network Management are what we are focusi= ng on now. Since we plan to organize a dedicated session in NMRG, all the d= iscussion will converge to the area of Network Management before Nov.

Just expect Stenio's output few days later : )

Yansen


> -----Original Message-----
> From: steniofernandes@gma= il.com [mailto:stenioferna= ndes@gmail.com] On
> Behalf Of Stenio Fernandes
> Sent: Tuesday, August 15, 2017 2:09 AM
> To: Haoyu song <haoyu.song= @huawei.com>
> Cc: yanshen <yanshen@huawei.c= om>; idnet@ietf.org
> Subject: Re: [Idnet] Summary 20170814 & IDN dedicated session call= for case
>
> Haoyu,
>
> I'm working on the anomaly detection use case and will send it to = the list this
> week.
>
> Stenio
>
> On Mon, Aug 14, 2017 at 12:47 PM, Haoyu song <haoyu.song@huawei.com>
> wrote:
> > Yansen,
> >
> >
> >
> > I see two key use cases are missing in the current list: root cau= se
> > analysis and anomaly detection. Those two are likely to use ML-ba= sed
> > solutions and the first one has already received a lot of researc= h.
> >
> >
> >
> > Haoyu
> >
> >
> >
> > From: IDNET [mailto:idn= et-bounces@ietf.org] On Behalf Of yanshen
> > Sent: Sunday, August 13, 2017 8:36 PM
> > To: idnet@ietf.org
> > Subject: [Idnet] Summary 20170814 & IDN dedicated session cal= l for
> > case
> >
> >
> >
> > Dear all,
> >
> >
> >
> > Here is a summary and some index (2017.08.14). Till now, whatever= the
> > case is supported or not, I tried to organize all the content and= keep
> > the core part. It is still welcome to contribute and discuss.
> >
> >
> >
> > If I miss something important, please let me know. Apologized in = advance.
> >
> >
> >
> > Yansen
> >
> >
> >
> >
> >
> > ---------=C2=A0 Roadmap=C2=A0 ---------
> >
> > ***Aug. : Collecting the use cases (related with NM). Rough thoug= hts
> > and requirements
> >
> > Sep. : Refining the cases and abstract the common elements
> >
> > Oct. : Deeply analysis. Especially on Data Format, control flow, = or
> > other key points
> >
> > Nov.: F2F discussions on IETF100
> >
> > ---------=C2=A0 Roadmap End=C2=A0 ---------
> >
> >
> >
> >
> >
> > 1. Gap and Requirement Analysis
> >
> >=C2=A0 =C2=A0 =C2=A01.1 Network Management requirement
> >
> >=C2=A0 =C2=A0 =C2=A01.2 TBD
> >
> > 2. Use Cases
> >
> >=C2=A0 =C2=A0 =C2=A02.1 Traffic Prediction
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Proposed by: yanshen@huawei.com
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Track:
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00131.html
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Abstract: Collect the history traffic data and
> > external data which may influence the traffic. Predict the traffi= c in
> > short/long/specific term. Avoid the congestion or risk in previou= sly.
> >
> >
> >
> >=C2=A0 =C2=A0 =C2=A02.2 QoS Management
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Proposed by: yanshen@huawei.com
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Track:
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00131.html
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Abstract: Use multiple paths to distribute the
> > traffic flows. Adjust the percentages. Avoid congestion and ensur= e QoS.
> >
> >
> >
> >=C2=A0 =C2=A0 =C2=A02.3 Application (and/or DDoS) detection
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Proposed by: aydinulas@gmx.net=
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Track:
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00133.html
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Abstract: Detect the application (or attack) from
> > network packets (HTTPS or plain) Collect the history traffic data= and
> > identify a service or attack (ex: Skype, Viber, DDoS attack etc.)=
> >
> >
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 2.4 QoE Management
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Proposed by: albert.cabell= os@gmail.com
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Track:
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00137.html
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Abstract: Collect low-level metrics (SNR, latency,
> > jitter, losses, etc) and measure QoE. Then use ML to understand w= hat
> > is the relation between satisfactory QoE and the low-level metric= s. As
> > an example learn that when delay>N then QoE is degraded, but w= hen
> > M<delay<N then QoE is satisfactory for the customers (pleas= e note that
> > QoE cannot be measured directly over your network). This is usefu= l to
> > understand how the network must be operated to provide satisfacto= ry QoE.
> >
> >
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 2.5 (Encrypted) Traffic Classif= ication
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Proposed by: jerome.francoi= s@inria.fr;
> > mskim16@etri.re.kr
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Track: [Jerome]
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00141.html ;
> > [Min-Suk Kim]
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00153.html
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Abstract:
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0[Jerome] collect flow-level traffic > > metrics such as protocol information but also meta metrics such a= s
> > distribution of packet sizes, inter-arrival times... Then use suc= h
> > information to label the traffic with the underlying application<= br> > > assuming that the granularity of classification may vary (type of=
> > application, exact application name,
> > version...)
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0[Min-Suk Kim] continuously collect pa= cket
> > data, then applying learning process for traffic classification w= ith
> > generating application using deep learning models such as CNN
> > (convolutional neural
> > network) and RNN (recurrent neural network). Data-set to apply in= to
> > the models are generated by processing with features of informati= on
> > from flow in packet data.
> >
> >
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 2.6 TBD
> >
> >
> >
> > 3. Data Focus
> >
> >=C2=A0 =C2=A0 =C2=A03.1 Data attribute
> >
> >=C2=A0 =C2=A0 =C2=A03.2 Data format
> >
> >=C2=A0 =C2=A0 =C2=A03.3 TBD
> >
> >
> >
> > 4. Support Technologies
> >
> >=C2=A0 =C2=A0 =C2=A04.1 Benchmarking Framework
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Proposed by: pedro@nict.go.jp > >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Track:
> > https://www.ietf.org/mail= -archive/web/idnet/current/msg00146.html
> >
> >=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2= =A0 Abstract: A proper benchmarking framework
> comprises
> > a set of reference procedures, methods, and models that can (or b= etter
> > *must*) be followed to assess the quality of an AI mechanism prop= osed
> > to be applied to the network management/control area. Moreover, a= nd
> > much more specific to the IDNET topics, is the inclusion, depende= ncy,
> > or just the general relation of a standard format enforced to the= data
> > that is used (input) and produced
> > (output) by the framework, so a kind of "data market" c= an arise
> > without requiring to transform the data. The initial scope of
> > input/output data would be the datasets, but also the new knowled= ge
> > items that are stated as a result of applying the benchmarking > > procedures defined by the framework, which can be collected toget= her
> > to build a database of benchmark results, or just contrasted with=
> > other existing entries in the database to know the position of th= e
> > solution just evaluated. This increases the usefulness of IDNET.<= br> > >
> >
> >
> >=C2=A0 =C2=A0 =C2=A04.2 TBD
> >
> >
> > _______________________________________________
> > IDNET mailing list
> > IDNET@ietf.org
> > https://www.ietf.org/mailman/listinfo/idne= t
> >
>
>
>
> --
> Prof. Stenio Fernandes
> CIn/UFPE
> http://www.steniofernandes.com



--
=
Prof. Stenio Fernandes
CIn/UFPE
--001a1148b9c8c192c805570a44c6-- From nobody Sun Aug 20 20:49:51 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id AE1571323A5 for ; Sun, 20 Aug 2017 20:49:49 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.699 X-Spam-Level: X-Spam-Status: No, score=-2.699 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id Bgw7Hm14Ystp for ; Sun, 20 Aug 2017 20:49:47 -0700 (PDT) Received: from mail-vk0-x22b.google.com (mail-vk0-x22b.google.com [IPv6:2607:f8b0:400c:c05::22b]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 6D8FD132344 for ; Sun, 20 Aug 2017 20:49:47 -0700 (PDT) Received: by mail-vk0-x22b.google.com with SMTP id n125so46655496vke.1 for ; Sun, 20 Aug 2017 20:49:47 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=KIPlq9uDJ5+/g3VJwl2K9GQFpkW27Mbn94PyINV0bO8=; b=mouuc9o7auden9SW9B3YUgDVSCzogmjWHTtmjBL54jVdBcJlV1+ytiHo0BosZtSss5 szn5I+/ydzSIRw93iJ1lvxK4Dc5cMOrCCghkewmvWdKHwfu7pKL66PjNuE8cdgBFG2MZ LfPEbHoGZ+4nyc0u9uKIR09hrdC3k90RPK0xl1JGIzvTQvseIDQYH0FKkfMFZMpv4Y6v CmCqt6ABgjWAlmwEf8kT2G/n8dH+6h5SikxBS0bAy/c/57uxjH6Jhjgix7k6nv+f7C+l TOl50dX5cHo8fgV5KBM91FOb/bOv6/34PyeZffZ/Chk6gXvbz3lcp50NeqMqyAFvi0ZB M4/A== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=KIPlq9uDJ5+/g3VJwl2K9GQFpkW27Mbn94PyINV0bO8=; b=jhj/rS905zwhzS+EhRaikFdcSIai4FQUaEyCMUSwFDB2bGTTAJhTn9PlRtfpZ3cc9S eY2FoR1BO6RaVmyikHQPwMOT+xJT/hxRai3BdCWLhXSzKlN5VuEzBrIZ2RAYAW1JEbVT /v5LW+QKmykMrfFdUJRxjZdOhCO3B7VFJd1IHXbyEgZgcV5x5kPlg0OFNF99iHmyBKDN uWhsQzwmxtHlOXUwDLh7t0NYYFLuT96hBOOWOLdZfZ1KmtdniTDQfdMCUvjnMAD9VNf+ gBNt5y9vEh7XJTvQC9qCwWbzwbmuKxqL1IcrKreqJlaE5DK4pZF7WxJBFloH9/B/lq+r Radw== X-Gm-Message-State: AHYfb5i2VwcpX5q73YzQE2qKGitBL+det2FasI9oYZBIY6eto7lTzp/y 4mjJ4ATmHDb87N/eSwo3dyBOPB/3QR+4 X-Received: by 10.31.150.6 with SMTP id y6mr9668175vkd.1.1503287386467; Sun, 20 Aug 2017 20:49:46 -0700 (PDT) MIME-Version: 1.0 Received: by 10.176.94.150 with HTTP; Sun, 20 Aug 2017 20:49:46 -0700 (PDT) In-Reply-To: <20170818061318.GG6804@spectre> References: <20170818061318.GG6804@spectre> From: Mahesh Govind Date: Mon, 21 Aug 2017 09:19:46 +0530 Message-ID: To: Pedro Martinez-Julia Cc: Albert Cabellos , Hirochika Asai , idnet@ietf.org Content-Type: multipart/alternative; boundary="001a1141f98caa73ba05573b5e17" Archived-At: Subject: Re: [Idnet] Public dataset X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 21 Aug 2017 03:49:50 -0000 --001a1141f98caa73ba05573b5e17 Content-Type: text/plain; charset="UTF-8" Thank you ... On Fri, Aug 18, 2017 at 11:43 AM, Pedro Martinez-Julia wrote: > Dear Albert and others, > > Since we have a GitHub repository to gather such information. We have a > "datasets" page [1] so, please, update it with these new items. Thank > you very much. > > Regards, > Pedro > > [1] https://github.com/pedromj/idnet/blob/master/datasets.md > > -- > Pedro Martinez-Julia > Network Science and Convergence Device Technology Laboratory > Network System Research Institute > National Institute of Information and Communications Technology (NICT) > 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan > Email: pedro@nict.go.jp > --------------------------------------------------------- > *** Entia non sunt multiplicanda praeter necessitatem *** > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a1141f98caa73ba05573b5e17 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Thank you ...

On Fri, Aug 18, 2017 at 11:43 AM, Pedro Martinez-Julia <= span dir=3D"ltr"><= pedro@nict.go.jp> wrote:
De= ar Albert and others,

Since we have a GitHub repository to gather such information. We have a
"datasets" page [1] so, please, update it with these new items. T= hank
you very much.

Regards,
Pedro

[1] https://github.com/pedromj/idnet/bl= ob/master/datasets.md

--
Pedro Martinez-Julia
Network Science and Convergence Device Technology Laboratory
Network System Research Institute
National Institute of Information and Communications Technology (NICT)
4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
Email: pedro@nict.go.jp
---------------------------------------------------------
*** Entia non sunt multiplicanda praeter necessitatem ***

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