From nobody Wed Sep 13 07:19:36 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 09A6E132930 for ; Wed, 13 Sep 2017 07:19:35 -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 NxUZ8E7tPxrS for ; Wed, 13 Sep 2017 07:19:27 -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 917DA124239 for ; Wed, 13 Sep 2017 07:19:26 -0700 (PDT) Received: from 172.18.7.190 (EHLO LHREML713-CAH.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DOM90457; Wed, 13 Sep 2017 14:19:24 +0000 (GMT) Received: from NKGEML412-HUB.china.huawei.com (10.98.56.73) by LHREML713-CAH.china.huawei.com (10.201.108.36) with Microsoft SMTP Server (TLS) id 14.3.301.0; Wed, 13 Sep 2017 15:19:24 +0100 Received: from NKGEML515-MBX.china.huawei.com ([fe80::a54a:89d2:c471:ff]) by nkgeml412-hub.china.huawei.com ([10.98.56.73]) with mapi id 14.03.0235.001; Wed, 13 Sep 2017 22:19:19 +0800 From: Sheng Jiang To: "idnet@ietf.org" Thread-Topic: Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions Thread-Index: AdMsmrntJhqPpIrCTTyzoU5Pp27WrA== Date: Wed, 13 Sep 2017 14:19:43 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-MBX.china.huawei.com> Accept-Language: en-GB, zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: 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bi9saXN0aW5mby9ubXJnDQo= From nobody Mon Sep 18 23:48:55 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 D1252133187 for ; Mon, 18 Sep 2017 23:48:53 -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 NWRjZFcuwwOw for ; Mon, 18 Sep 2017 23:48:51 -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 CEC1C1342C8 for ; Mon, 18 Sep 2017 23:48:49 -0700 (PDT) Received: from 172.18.7.190 (EHLO LHREML713-CAH.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DOV72591; Tue, 19 Sep 2017 06:48:48 +0000 (GMT) Received: from DGGEMM405-HUB.china.huawei.com (10.3.20.213) by LHREML713-CAH.china.huawei.com (10.201.108.36) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 19 Sep 2017 07:48:47 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.237]) by DGGEMM405-HUB.china.huawei.com ([10.3.20.213]) with mapi id 14.03.0301.000; Tue, 19 Sep 2017 14:48:24 +0800 From: yanshen To: Sheng Jiang , "idnet@ietf.org" Thread-Topic: Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions Thread-Index: AdMsmrntJhqPpIrCTTyzoU5Pp27WrAEdz9xg Date: Tue, 19 Sep 2017 06:48:24 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D2999C19@DGGEMM505-MBX.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-MBX.china.huawei.com> In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-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: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020206.59C0BDD0.0091, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.237, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 52b3f5163f4bd028c5da07a08cc07898 Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions 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, 19 Sep 2017 06:48:54 -0000 Hi Sheng,=20 I would like to have a short presentation about the Use case of Traffic Pre= diction/QoS Model.=20 My question is how to "register"? I directly send Email to the NMRG chair o= r we have a pre-registration in IDNet ? I attach the brief summary of use cases in the end. Hope it helpful.=20 Yansen =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 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 in= fluence the traffic. Predict the traffic in short/long/specific term. Avoid= the congestion or risk in previously. =09 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.=20 =09 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.) =09 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) a= nd measure QoE. Then use ML to understand what is the relation between sati= sfactory QoE and the low-level metrics. As an example learn that when delay= >N then QoE is degraded, but when M -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Sheng Jiang > Sent: Wednesday, September 13, 2017 10:20 PM > To: idnet@ietf.org > Subject: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMR= G > meeting: Call for Contributions >=20 > Hi, IDNet, >=20 > After coordinating with NMRG chairs, a Call for Contributions message (se= e > below) has been sent by them to the NMRG mailing list regarding to the to= pic > of applying AI into network management. This is in line with our earlier > discussion to have a session on this in NMRG, Singapore. You could send e= mail > to volunteer for presentations in either NMRG or IDNet mailing list (I wi= ll bridge > to NMRG chairs in the IDNet case) or cross post. >=20 > Looking forward for your contributions and good discussion in Singapore. >=20 > Best regards, >=20 > Sheng >=20 > -----Original Message----- > From: nmrg [mailto:nmrg-bounces@irtf.org] On Behalf Of Lisandro > Zambenedetti Granville > Sent: Wednesday, September 13, 2017 9:57 PM > To: nmrg@irtf.org > Subject: [nmrg] 45th NMRG meeting: Call for Contributions >=20 > Call for Contributions > 45th NMRG meeting at IETF 100 >=20 > In the next IETF100/Singapore we will be organizing the 45th NMRG meeting= . > We would like to center the upcoming meeting around the use of artificial > intelligence (AI) for network management, including related topics as div= erse > as machine-learning and intelligent-defined networks, for example. >=20 > AI for network management is not a new topic, as can be easily observed i= n the > literature produced by the network management community already years ago= . > On the other hand, AI has matured a lot, finding applications is several = areas. > People interested in the subject also formed communities that can contrib= ute > too. As such, revisiting AI for network management is not only appropriat= e but > also timely. >=20 > In this Call for Contributions we would like to receive proposals of > presentations/discussions for the upcoming 45th NMRG meeting. That includ= es, > for example: >=20 > - Use cases where AI could/should be used in network management > - Real-life experiments, results, and findings on AI for network manageme= nt > - Disruptive and/or new management paradigms based on AI > - Potential standard requirements for applying AI for network management > - Both preliminary and mature approaches >=20 > Please contribute and feel free to distribute this call to other mailing = lists > whose members you believe would be interested and could contribute too. >=20 > Best regards, Lisandro and Laurent. > _______________________________________________ > nmrg mailing list > nmrg@irtf.org > https://www.irtf.org/mailman/listinfo/nmrg > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet From nobody Tue Sep 19 00:34:09 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 42357133057 for ; 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FPR:; SPF:None; PTR:InfoNoRecords; MX:1; A:0; LANG:en; received-spf: None (protection.outlook.com: nokia-bell-labs.com does not designate permitted sender hosts) spamdiagnosticoutput: 1:99 spamdiagnosticmetadata: NSPM Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 X-OriginatorOrg: nokia-bell-labs.com X-MS-Exchange-CrossTenant-originalarrivaltime: 19 Sep 2017 07:34:02.6487 (UTC) X-MS-Exchange-CrossTenant-fromentityheader: Hosted X-MS-Exchange-CrossTenant-id: 5d471751-9675-428d-917b-70f44f9630b0 X-MS-Exchange-Transport-CrossTenantHeadersStamped: HE1PR0701MB1770 Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions 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, 19 Sep 2017 07:34:08 -0000 Dear Yansen, all, We (NMRG chairs) will coordinate with Sheng/IDNET for defining the agenda. Please send your proposal to either lists. Thanks, Laurent. -----Original Message----- From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen Sent: Tuesday, September 19, 2017 8:48 AM To: Sheng Jiang ; idnet@ietf.org Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th N= MRG meeting: Call for Contributions Hi Sheng,=20 I would like to have a short presentation about the Use case of Traffic Pre= diction/QoS Model.=20 My question is how to "register"? I directly send Email to the NMRG chair o= r we have a pre-registration in IDNet ? I attach the brief summary of use cases in the end. Hope it helpful.=20 Yansen =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 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 in= fluence the traffic. Predict the traffic in short/long/specific term. Avoid= the congestion or risk in previously. =09 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.=20 =09 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.) =09 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) a= nd measure QoE. Then use ML to understand what is the relation between sati= sfactory QoE and the low-level metrics. As an example learn that when delay= >N then QoE is degraded, but when M -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Sheng Jiang > Sent: Wednesday, September 13, 2017 10:20 PM > To: idnet@ietf.org > Subject: [Idnet] Applying AI into network management//FW: [nmrg] 45th=20 > NMRG > meeting: Call for Contributions >=20 > Hi, IDNet, >=20 > After coordinating with NMRG chairs, a Call for Contributions message=20 > (see > below) has been sent by them to the NMRG mailing list regarding to the=20 > topic of applying AI into network management. This is in line with our=20 > earlier discussion to have a session on this in NMRG, Singapore. You=20 > could send email to volunteer for presentations in either NMRG or=20 > IDNet mailing list (I will bridge to NMRG chairs in the IDNet case) or cr= oss post. >=20 > Looking forward for your contributions and good discussion in Singapore. >=20 > Best regards, >=20 > Sheng >=20 > -----Original Message----- > From: nmrg [mailto:nmrg-bounces@irtf.org] On Behalf Of Lisandro=20 > Zambenedetti Granville > Sent: Wednesday, September 13, 2017 9:57 PM > To: nmrg@irtf.org > Subject: [nmrg] 45th NMRG meeting: Call for Contributions >=20 > Call for Contributions > 45th NMRG meeting at IETF 100 >=20 > In the next IETF100/Singapore we will be organizing the 45th NMRG meeting= . > We would like to center the upcoming meeting around the use of=20 > artificial intelligence (AI) for network management, including related=20 > topics as diverse as machine-learning and intelligent-defined networks, f= or example. >=20 > AI for network management is not a new topic, as can be easily=20 > observed in the literature produced by the network management community a= lready years ago. > On the other hand, AI has matured a lot, finding applications is several = areas. > People interested in the subject also formed communities that can=20 > contribute too. As such, revisiting AI for network management is not=20 > only appropriate but also timely. >=20 > In this Call for Contributions we would like to receive proposals of=20 > presentations/discussions for the upcoming 45th NMRG meeting. That=20 > includes, for example: >=20 > - Use cases where AI could/should be used in network management > - Real-life experiments, results, and findings on AI for network=20 > management > - Disruptive and/or new management paradigms based on AI > - Potential standard requirements for applying AI for network=20 > management > - Both preliminary and mature approaches >=20 > Please contribute and feel free to distribute this call to other=20 > mailing lists whose members you believe would be interested and could con= tribute too. >=20 > Best regards, Lisandro and Laurent. > _______________________________________________ > nmrg mailing list > nmrg@irtf.org > https://www.irtf.org/mailman/listinfo/nmrg > _______________________________________________ > 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 From nobody Tue Sep 19 00:43:39 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 F0D4313208E for ; Tue, 19 Sep 2017 00:43:36 -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 3Avuy1F_Ne3P for ; Tue, 19 Sep 2017 00:43:34 -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 EF120126DFE for ; Tue, 19 Sep 2017 00:43:33 -0700 (PDT) Received: from 172.18.7.190 (EHLO LHREML712-CAH.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DVT05681; Tue, 19 Sep 2017 07:43:31 +0000 (GMT) Received: from DGGEMM404-HUB.china.huawei.com (10.3.20.212) by LHREML712-CAH.china.huawei.com (10.201.108.35) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 19 Sep 2017 08:43:30 +0100 Received: from DGGEMM505-MBX.china.huawei.com ([169.254.1.237]) by DGGEMM404-HUB.china.huawei.com ([10.3.20.212]) with mapi id 14.03.0301.000; Tue, 19 Sep 2017 15:41:47 +0800 From: yanshen To: "Ciavaglia, Laurent (Nokia - FR/Nozay)" , Sheng Jiang , "idnet@ietf.org" Thread-Topic: Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions Thread-Index: AdMsmrntJhqPpIrCTTyzoU5Pp27WrAEdz9xgAAHVMtAAAFGo8A== Date: Tue, 19 Sep 2017 07:41:46 +0000 Message-ID: <6AE399511121AB42A34ACEF7BF25B4D2999C90@DGGEMM505-MBX.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D2999C19@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: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020203.59C0CAA4.005D, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.1.237, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 0861a0ca866d4d0aada492a9c88cb69a Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions 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, 19 Sep 2017 07:43:37 -0000 Dear Laurent, Thanks for your answer.=20 Yansen > -----Original Message----- > From: Ciavaglia, Laurent (Nokia - FR/Nozay) > [mailto:laurent.ciavaglia@nokia-bell-labs.com] > Sent: Tuesday, September 19, 2017 3:34 PM > To: yanshen ; Sheng Jiang ; > idnet@ietf.org > Subject: RE: Applying AI into network management//FW: [nmrg] 45th NMRG > meeting: Call for Contributions >=20 > Dear Yansen, all, >=20 > We (NMRG chairs) will coordinate with Sheng/IDNET for defining the agenda= . > Please send your proposal to either lists. >=20 > Thanks, Laurent. >=20 >=20 > -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen > Sent: Tuesday, September 19, 2017 8:48 AM > To: Sheng Jiang ; idnet@ietf.org > Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th > NMRG meeting: Call for Contributions >=20 > Hi Sheng, >=20 > I would like to have a short presentation about the Use case of Traffic > Prediction/QoS Model. >=20 > My question is how to "register"? I directly send Email to the NMRG chair= or > we have a pre-registration in IDNet ? >=20 > I attach the brief summary of use cases in the end. Hope it helpful. >=20 > Yansen >=20 >=20 > =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 > 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. A= void the > congestion or risk in previously. >=20 > 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 th= e > percentages. Avoid congestion and ensure QoS. >=20 > 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.) >=20 > 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 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 t= o > provide satisfactory QoE. >=20 > 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 wit= h the > underlying application assuming that the granularity of classification ma= y 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 u= sing deep > learning models such as CNN (convolutional neural network) and RNN > (recurrent neural network). Data-set to apply into the models are generat= ed by > precessing with features of information from flow in packet data. >=20 > 2.6 Anomaly Detection > Proposed by: steniofernandes@gmail.com > Track: > https://www.ietf.org/mail-archive/web/idnet/current/msg00186.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 wit= h the > underlying application assuming that the granularity of classification ma= y 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 u= sing deep > learning models such as CNN (convolutional neural network) and RNN > (recurrent neural network). Data-set to apply into the models are generat= ed by > precessing with features of information from flow in packet data. >=20 > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD >=20 > 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 relat= ion of a > standard format enforced to the data that is used (input) and produced (o= utput) > 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 cont= rasted > with other existing entries in the database to know the position of the s= olution > just evaluated. This increases the usefulness of IDNET. >=20 > 4.2 TBD >=20 > =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 >=20 > > -----Original Message----- > > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Sheng Jiang > > Sent: Wednesday, September 13, 2017 10:20 PM > > To: idnet@ietf.org > > Subject: [Idnet] Applying AI into network management//FW: [nmrg] 45th > > NMRG > > meeting: Call for Contributions > > > > Hi, IDNet, > > > > After coordinating with NMRG chairs, a Call for Contributions message > > (see > > below) has been sent by them to the NMRG mailing list regarding to the > > topic of applying AI into network management. This is in line with our > > earlier discussion to have a session on this in NMRG, Singapore. You > > could send email to volunteer for presentations in either NMRG or > > IDNet mailing list (I will bridge to NMRG chairs in the IDNet case) or = cross > post. > > > > Looking forward for your contributions and good discussion in Singapore= . > > > > Best regards, > > > > Sheng > > > > -----Original Message----- > > From: nmrg [mailto:nmrg-bounces@irtf.org] On Behalf Of Lisandro > > Zambenedetti Granville > > Sent: Wednesday, September 13, 2017 9:57 PM > > To: nmrg@irtf.org > > Subject: [nmrg] 45th NMRG meeting: Call for Contributions > > > > Call for Contributions > > 45th NMRG meeting at IETF 100 > > > > In the next IETF100/Singapore we will be organizing the 45th NMRG meeti= ng. > > We would like to center the upcoming meeting around the use of > > artificial intelligence (AI) for network management, including related > > topics as diverse as machine-learning and intelligent-defined networks,= for > example. > > > > AI for network management is not a new topic, as can be easily > > observed in the literature produced by the network management community > already years ago. > > On the other hand, AI has matured a lot, finding applications is severa= l areas. > > People interested in the subject also formed communities that can > > contribute too. As such, revisiting AI for network management is not > > only appropriate but also timely. > > > > In this Call for Contributions we would like to receive proposals of > > presentations/discussions for the upcoming 45th NMRG meeting. That > > includes, for example: > > > > - Use cases where AI could/should be used in network management > > - Real-life experiments, results, and findings on AI for network > > management > > - Disruptive and/or new management paradigms based on AI > > - Potential standard requirements for applying AI for network > > management > > - Both preliminary and mature approaches > > > > Please contribute and feel free to distribute this call to other > > mailing lists whose members you believe would be interested and could > contribute too. > > > > Best regards, Lisandro and Laurent. > > _______________________________________________ > > nmrg mailing list > > nmrg@irtf.org > > https://www.irtf.org/mailman/listinfo/nmrg > > _______________________________________________ > > 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 From nobody Tue Sep 19 12:06:49 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 2DE58126BF3 for ; Tue, 19 Sep 2017 12:06:32 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.91 X-Spam-Level: X-Spam-Status: No, score=-2.91 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_NONE=-0.0001, RCVD_IN_MSPIKE_H5=-1, 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 3qSEboYjoxY0 for ; Tue, 19 Sep 2017 12:06:29 -0700 (PDT) Received: from EUR01-HE1-obe.outbound.protection.outlook.com (mail-he1eur01on0115.outbound.protection.outlook.com [104.47.0.115]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 8F22213436F for ; Tue, 19 Sep 2017 12:06:28 -0700 (PDT) Received: from DB6PR0601MB2167.eurprd06.prod.outlook.com (10.168.57.26) by DB6PR0601MB2069.eurprd06.prod.outlook.com (10.168.56.150) with Microsoft SMTP Server (version=TLS1_2, cipher=TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384_P256) id 15.20.56.11; Tue, 19 Sep 2017 19:06:25 +0000 Received: from DB6PR0601MB2167.eurprd06.prod.outlook.com ([fe80::29a6:d86f:ad2:e821]) by DB6PR0601MB2167.eurprd06.prod.outlook.com ([fe80::29a6:d86f:ad2:e821%15]) with mapi id 15.20.0056.016; Tue, 19 Sep 2017 19:06:25 +0000 From: "Diego R. Lopez" To: "Ciavaglia, Laurent (Nokia - FR/Nozay)" , yanshen , "Sheng Jiang" , "idnet@ietf.org" Thread-Topic: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions Thread-Index: AQHTMXpjANhJ2zgnkkuzzkNvYOK7mA== Date: Tue, 19 Sep 2017 19:06:25 +0000 Message-ID: <7C7632E4-0225-46F0-A7B6-E872D424CDE2@telefonica.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: user-agent: Microsoft-MacOutlook/f.26.0.170902 authentication-results: spf=none (sender IP is ) smtp.mailfrom=diego.r.lopez@telefonica.com; x-originating-ip: [78.22.24.11] x-ms-publictraffictype: Email x-microsoft-exchange-diagnostics: 1; DB6PR0601MB2069; 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Lopez" , "Ciavaglia, Laurent (Nokia - FR/Nozay)" , yanshen , "idnet@ietf.org" Thread-Topic: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions Thread-Index: AQHTMXpjJhqPpIrCTTyzoU5Pp27WrKK9EiDw Date: Wed, 20 Sep 2017 03:28:52 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CEC27DC@NKGEML515-MBX.china.huawei.com> References: <7C7632E4-0225-46F0-A7B6-E872D424CDE2@telefonica.com> In-Reply-To: <7C7632E4-0225-46F0-A7B6-E872D424CDE2@telefonica.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=0001.0A020206.59C1E084.0007, 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: 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Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions 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, 20 Sep 2017 13:00:35 -0000 --089e082652986709b605599e8f1f Content-Type: text/plain; charset="UTF-8" Hi all We have been working on using a deep-reinforcement learning agent to automatically achieve optimal routing configuration. We demonstrate the efficiency of the agent by means of simulations, we will soon (2 weeks aprox) make the results public. The results might be interesting for the IDNET community at large since, at the best of our knowledge, this is the first use of deep-reinforcement learning for route optimization. In addition to this, the agent uses a reward function that must be set by the operator. This function describes in mathematical terms the desired state of the network, for instance to load-balance traffic among the links. The agent then aims to maximize the reward function. This function actually represents the policy set by the orchestrator. In my honest opinion there is an interesting discussion on how to express such functions in terms of management policy, this might be relevant for the NMRG community. Albert On Tue, Sep 19, 2017 at 4:34 PM, Ciavaglia, Laurent (Nokia - FR/Nozay) < laurent.ciavaglia@nokia-bell-labs.com> wrote: > Dear Yansen, all, > > We (NMRG chairs) will coordinate with Sheng/IDNET for defining the agenda. > Please send your proposal to either lists. > > Thanks, Laurent. > > > -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen > Sent: Tuesday, September 19, 2017 8:48 AM > To: Sheng Jiang ; idnet@ietf.org > Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th > NMRG meeting: Call for Contributions > > Hi Sheng, > > I would like to have a short presentation about the Use case of Traffic > Prediction/QoS Model. > > My question is how to "register"? I directly send Email to the NMRG chair > or we have a pre-registration in IDNet ? > > I attach the brief summary of use cases in the end. Hope it helpful. > > Yansen > > > ========================================== > 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 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. > > 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 precessing with features of information from flow > in packet data. > > 2.6 Anomaly Detection > Proposed by: steniofernandes@gmail.com > Track: https://www.ietf.org/mail- > archive/web/idnet/current/msg00186.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 precessing with features of information from flow > in packet data. > > 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 > > ========================================= > > > -----Original Message----- > > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Sheng Jiang > > Sent: Wednesday, September 13, 2017 10:20 PM > > To: idnet@ietf.org > > Subject: [Idnet] Applying AI into network management//FW: [nmrg] 45th > > NMRG > > meeting: Call for Contributions > > > > Hi, IDNet, > > > > After coordinating with NMRG chairs, a Call for Contributions message > > (see > > below) has been sent by them to the NMRG mailing list regarding to the > > topic of applying AI into network management. This is in line with our > > earlier discussion to have a session on this in NMRG, Singapore. You > > could send email to volunteer for presentations in either NMRG or > > IDNet mailing list (I will bridge to NMRG chairs in the IDNet case) or > cross post. > > > > Looking forward for your contributions and good discussion in Singapore. > > > > Best regards, > > > > Sheng > > > > -----Original Message----- > > From: nmrg [mailto:nmrg-bounces@irtf.org] On Behalf Of Lisandro > > Zambenedetti Granville > > Sent: Wednesday, September 13, 2017 9:57 PM > > To: nmrg@irtf.org > > Subject: [nmrg] 45th NMRG meeting: Call for Contributions > > > > Call for Contributions > > 45th NMRG meeting at IETF 100 > > > > In the next IETF100/Singapore we will be organizing the 45th NMRG > meeting. > > We would like to center the upcoming meeting around the use of > > artificial intelligence (AI) for network management, including related > > topics as diverse as machine-learning and intelligent-defined networks, > for example. > > > > AI for network management is not a new topic, as can be easily > > observed in the literature produced by the network management community > already years ago. > > On the other hand, AI has matured a lot, finding applications is several > areas. > > People interested in the subject also formed communities that can > > contribute too. As such, revisiting AI for network management is not > > only appropriate but also timely. > > > > In this Call for Contributions we would like to receive proposals of > > presentations/discussions for the upcoming 45th NMRG meeting. That > > includes, for example: > > > > - Use cases where AI could/should be used in network management > > - Real-life experiments, results, and findings on AI for network > > management > > - Disruptive and/or new management paradigms based on AI > > - Potential standard requirements for applying AI for network > > management > > - Both preliminary and mature approaches > > > > Please contribute and feel free to distribute this call to other > > mailing lists whose members you believe would be interested and could > contribute too. > > > > Best regards, Lisandro and Laurent. > > _______________________________________________ > > nmrg mailing list > > nmrg@irtf.org > > https://www.irtf.org/mailman/listinfo/nmrg > > _______________________________________________ > > 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 > --089e082652986709b605599e8f1f Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable

Hi all

We have been working on using a deep-reinforcement le= arning agent to automatically achieve optimal routing configuration. We dem= onstrate the efficiency of the agent by means of simulations, we will soon = (2 weeks aprox) make the results public.

The results might be interesting for the IDNET commun= ity at large since, at the best of our knowledge, this is the first use of = deep-reinforcement learning for route optimization.

In addition to this, the agent uses a reward function= that must be set by the operator. This function describes in mathematical = terms the desired state of the network, for instance to load-balance traffi= c among the links. The agent then aims to maximize the reward function.

This function actually represents the policy set by t= he orchestrator. In my honest opinion there is an interesting discussion on= how to express such functions in terms of management policy, this might be= relevant for the NMRG community.

Albert


On Tue, Sep 19, 2017 at 4:34 PM, Ciavaglia, Laurent (Nokia= - FR/Nozay) <laurent.ciavaglia@nokia-bell-labs.com> wrote:
Dear Yansen, all,

We (NMRG chairs) will coordinate with Sheng/IDNET for defining the agenda.<= br> Please send your proposal to either lists.

Thanks, Laurent.


-----Original Message-----
From: IDNET [mailto:idnet-bounces= @ietf.org] On Behalf Of yanshen
Sent: Tuesday, September 19, 2017 8:48 AM
To: Sheng Jiang <jiangsheng@hua= wei.com>; idnet@ietf.org
Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th N= MRG meeting: Call for Contributions

Hi Sheng,

I would like to have a short presentation about the Use case of Traffic Pre= diction/QoS Model.

My question is how to "register"? I directly send Email to the NM= RG chair or we have a pre-registration in IDNet ?

I attach the brief summary of use cases in the end. Hope it helpful.

Yansen


=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
1. Gap and Requirement Analysis
=C2=A0 =C2=A0 1.1 Network Management requirement
=C2=A0 =C2=A0 1.2 TBD
2. Use Cases
=C2=A0 =C2=A0 2.1 Traffic Prediction
=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 Track: https://www.ietf.org/mail-archive/web/idne= t/current/msg00131.html
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Abstract: Collect t= he history traffic data and external data which may influence the traffic. = Predict the traffic in short/long/specific term. Avoid the congestion or ri= sk in previously.

=C2=A0 =C2=A0 2.2 QoS Management
=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 Track: https://www.ietf.org/mail-archive/web/idne= t/current/msg00131.html
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Abstract: Use multi= ple paths to distribute the traffic flows. Adjust the percentages. Avoid co= ngestion and ensure QoS.

=C2=A0 =C2=A0 2.3 Application (and/or DDoS) detection
=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 Track: https://www.ietf.org/mail-archive/web/idne= t/current/msg00133.html
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Abstract: Detect th= e application (or attack) from network packets (HTTPS or plain) Collect the= history traffic data and identify a service or attack (ex: Skype, Viber, D= DoS attack etc.)

=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 Proposed by: albert.cabellos@gmail.com
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Track: https://www.ietf.org/mail-archive/web/idne= t/current/msg00137.html
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Abstract: Collect l= ow-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 deg= raded, 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 satisfa= ctory QoE.

=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 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 Track: [Jerome] https://www.ietf.org/mail-archive= /web/idnet/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/cur= rent/msg00153.html
=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 [Jerome] collect flow-level traffic metrics such as protocol inf= ormation but also meta metrics such as distribution of packet sizes, inter-= arrival times... Then use such information to label the traffic with the un= derlying application assuming that the granularity of classification may va= ry (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 [Min-Suk Kim]continuously collect packet data, then applying lea= rning process for traffic classification with generating application using = deep learning models such as CNN (convolutional neural network) and RNN (re= current neural network). Data-set to apply into the models are generated by= precessing with features of information from flow in packet data.

=C2=A0 =C2=A0 =C2=A0 =C2=A0 2.6 Anomaly Detection
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Proposed by: steniofernandes@gmail.com
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 Track: https://www.ietf.org/mail-archive/web/idne= t/current/msg00186.html
=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 [Jerome] collect flow-level traffic metrics such as protocol inf= ormation but also meta metrics such as distribution of packet sizes, inter-= arrival times... Then use such information to label the traffic with the un= derlying application assuming that the granularity of classification may va= ry (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 [Min-Suk Kim]continuously collect packet data, then applying lea= rning process for traffic classification with generating application using = deep learning models such as CNN (convolutional neural network) and RNN (re= current neural network). Data-set to apply into the models are generated by= precessing with features of information from flow in packet data.

3. Data Focus
=C2=A0 =C2=A0 3.1 Data attribute
=C2=A0 =C2=A0 3.2 Data format
=C2=A0 =C2=A0 3.3 TBD

4. Support Technologies
=C2=A0 =C2=A0 4.1 Benchmarking Framework
=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 Track: https://www.ietf.org/mail-archive/web/idne= t/current/msg00146.html
=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, an= d models that can (or better *must*) be followed to assess the quality of a= n AI mechanism proposed to be applied to the network management/control are= a. Moreover, and much more specific to the IDNET topics, is the inclusion, = dependency, or just the general relation of a standard format enforced to t= he data that is used (input) and produced (output) by the framework, so a k= ind of "data market" can arise without requiring to transform the= data. The initial scope of input/output data would be the datasets, but al= so the new knowledge items that are stated as a result of applying the benc= hmarking procedures defined by the framework, which can be collected togeth= er 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.

=C2=A0 =C2=A0 4.2 TBD

=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

> -----Original Message-----
> From: IDNET [mailto:idnet-bo= unces@ietf.org] On Behalf Of Sheng Jiang
> Sent: Wednesday, September 13, 2017 10:20 PM
> To: idnet@ietf.org
> Subject: [Idnet] Applying AI into network management//FW: [nmrg] 45th<= br> > NMRG
> meeting: Call for Contributions
>
> Hi, IDNet,
>
> After coordinating with NMRG chairs, a Call for Contributions message<= br> > (see
> below) has been sent by them to the NMRG mailing list regarding to the=
> topic of applying AI into network management. This is in line with our=
> earlier discussion to have a session on this in NMRG, Singapore. You > could send email to volunteer for presentations in either NMRG or
> IDNet mailing list (I will bridge to NMRG chairs in the IDNet case) or= cross post.
>
> Looking forward for your contributions and good discussion in Singapor= e.
>
> Best regards,
>
> Sheng
>
> -----Original Message-----
> From: nmrg [mailto:nmrg-bounc= es@irtf.org] On Behalf Of Lisandro
> Zambenedetti Granville
> Sent: Wednesday, September 13, 2017 9:57 PM
> To: nmrg@irtf.org
> Subject: [nmrg] 45th NMRG meeting: Call for Contributions
>
> Call for Contributions
> 45th NMRG meeting at IETF 100
>
> In the next IETF100/Singapore we will be organizing the 45th NMRG meet= ing.
> We would like to center the upcoming meeting around the use of
> artificial intelligence (AI) for network management, including related=
> topics as diverse as machine-learning and intelligent-defined networks= , for example.
>
> AI for network management is not a new topic, as can be easily
> observed in the literature produced by the network management communit= y already years ago.
> On the other hand, AI has matured a lot, finding applications is sever= al areas.
> People interested in the subject also formed communities that can
> contribute too. As such, revisiting AI for network management is not > only appropriate but also timely.
>
> In this Call for Contributions we would like to receive proposals of > presentations/discussions for the upcoming 45th NMRG meeting. That
> includes, for example:
>
> - Use cases where AI could/should be used in network management
> - Real-life experiments, results, and findings on AI for network
> management
> - Disruptive and/or new management paradigms based on AI
> - Potential standard requirements for applying AI for network
> management
> - Both preliminary and mature approaches
>
> Please contribute and feel free to distribute this call to other
> mailing lists whose members you believe would be interested and could = contribute too.
>
> Best regards, Lisandro and Laurent.
> _______________________________________________
> nmrg mailing list
> nmrg@irtf.org
> https://www.irtf.org/mailman/listinfo/nmrg > _______________________________________________
> 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

--089e082652986709b605599e8f1f-- From nobody Wed Sep 20 06:21:58 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 0BED51332D5 for ; Wed, 20 Sep 2017 06:21:56 -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 vA3EhIp8lwH3 for ; Wed, 20 Sep 2017 06:21:52 -0700 (PDT) Received: from mail-wr0-x22c.google.com (mail-wr0-x22c.google.com [IPv6:2a00:1450:400c:c0c::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 53447133053 for ; Wed, 20 Sep 2017 06:21:51 -0700 (PDT) Received: by mail-wr0-x22c.google.com with SMTP id u96so2153805wrb.6 for ; Wed, 20 Sep 2017 06:21:51 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=from:message-id:mime-version:subject:date:in-reply-to:cc:to :references; bh=an3hGJaKttd8X2keGMMx6axyauFwa8Bo7NuHMZNa4EM=; b=gksXo4xTf5VfMqwXiw3q+UswiOhOtrEEk6KA/V6N2KvBixV+PLxob6RbNCA+FF/iva cYTEa6X9plwqQwBqVd2eKnyykhUiU91zKQZ0YXyjBL7euhCKBRVG7B9oG74QfWmIgKm2 WLFbRzXfn5tSH+km97xb5ZIj4Ug7I1N1rP2IYS1kBU+UgkX7ZOhKSIJcInI6N8TB9yQW mn1CeMaGX9Jb0vTU6N13UcS4ExYwVH4gnHlnZLHdnNktdzZC2MaUTaz4bLnCN8iuLF5l KUKRdMUEbZdAno4ocahFPv32EzG7pEhtSKKJAUyxWOO9JWoNsrYhwCaJyKPkjJwPZ/JZ MkZg== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:from:message-id:mime-version:subject:date :in-reply-to:cc:to:references; bh=an3hGJaKttd8X2keGMMx6axyauFwa8Bo7NuHMZNa4EM=; b=GLy5Bs6SDHJhmLX3tRPiG/DsV52bSElijwFR4ll670Zf9cWjxOEKtWuSKCl0Nhc/qo /1Ftq3UlkBxsxR37fm1ken5YVQ5GOrOz7aOq+9gm7DWEabzP1jggNnfiF+xUb9gdmARp dNSpxdtACQDhTUAMup7nvtwL/w6HHMY/TXZ4EfuIAU9Bf3WbZ2z7nm17TQ3KaAUkdr0Y cwwVy8MD/m1W98xLWK3vSPWtWDjCcl8q0ohIYD+GZnJGuPgUM5USZW30YzJn7S4boOUj ZZ1EnWDbiJKCswwJcijbnwWaRnIkeuowx1xKo61vJXn4AwwVZynTFWWyzXe66Riao2Wz 2oTg== X-Gm-Message-State: AHPjjUjrfW8TLWRLEgI8YH7up2+gkCWs8TYpPxfsbQZC0kOcJznn1MWz 2rgXGQ+Ek7CF0ZFCcXb5vZVjsRNB X-Google-Smtp-Source: AOwi7QBNrappc3xCZo6Sl0hvsYrXGbP0nNzkdnyloxhwRWhwWtPw0DVLwdtDz8+McGfTmTij2lfSVQ== X-Received: by 10.223.136.48 with SMTP id d45mr4172940wrd.200.1505913709454; Wed, 20 Sep 2017 06:21:49 -0700 (PDT) Received: from g039h008.guest.ugent.be ([193.190.84.1]) by smtp.gmail.com with ESMTPSA id w2sm1894356wrb.67.2017.09.20.06.21.46 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Wed, 20 Sep 2017 06:21:47 -0700 (PDT) From: Alex Galis Message-Id: Content-Type: multipart/alternative; boundary="Apple-Mail=_130822D4-91B5-4F0C-B5E7-EBCBE87BA623" Mime-Version: 1.0 (Mac OS X Mail 10.3 \(3273\)) Date: Wed, 20 Sep 2017 14:21:45 +0100 In-Reply-To: Cc: "Ciavaglia, Laurent (Nokia - FR/Nozay)" , yanshen , Sheng Jiang , Albert Cabellos To: "idnet@ietf.org" References: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D2999C19@DGGEMM505-MBX.china.huawei.com> X-Mailer: Apple Mail (2.3273) Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions 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, 20 Sep 2017 13:21:56 -0000 --Apple-Mail=_130822D4-91B5-4F0C-B5E7-EBCBE87BA623 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 Hi All Re: deep learning survey and tutorial = http://web.cse.ohio-state.edu/~panda.2/hoti17-dl.pdf = You may be interested in the deep learning survey and tutorial presented = recently at Interconnects =E2=80=992017 Best Regards Alex > On 20 Sep 2017, at 14:00, Albert Cabellos = wrote: >=20 > Hi all >=20 > We have been working on using a deep-reinforcement learning agent to = automatically achieve optimal routing configuration. We demonstrate the = efficiency of the agent by means of simulations, we will soon (2 weeks = aprox) make the results public. >=20 > The results might be interesting for the IDNET community at large = since, at the best of our knowledge, this is the first use of = deep-reinforcement learning for route optimization. >=20 > In addition to this, the agent uses a reward function that must be set = by the operator. This function describes in mathematical terms the = desired state of the network, for instance to load-balance traffic among = the links. The agent then aims to maximize the reward function. >=20 > This function actually represents the policy set by the orchestrator. = In my honest opinion there is an interesting discussion on how to = express such functions in terms of management policy, this might be = relevant for the NMRG community. >=20 > Albert >=20 >=20 > On Tue, Sep 19, 2017 at 4:34 PM, Ciavaglia, Laurent (Nokia - FR/Nozay) = > wrote: > Dear Yansen, all, >=20 > We (NMRG chairs) will coordinate with Sheng/IDNET for defining the = agenda. > Please send your proposal to either lists. >=20 > Thanks, Laurent. >=20 >=20 > -----Original Message----- > From: IDNET [mailto:idnet-bounces@ietf.org = ] On Behalf Of yanshen > Sent: Tuesday, September 19, 2017 8:48 AM > To: Sheng Jiang >; idnet@ietf.org > Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] = 45th NMRG meeting: Call for Contributions >=20 > Hi Sheng, >=20 > I would like to have a short presentation about the Use case of = Traffic Prediction/QoS Model. >=20 > My question is how to "register"? I directly send Email to the NMRG = chair or we have a pre-registration in IDNet ? >=20 > I attach the brief summary of use cases in the end. Hope it helpful. >=20 > Yansen >=20 >=20 > =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 > 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. >=20 > 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. >=20 > 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.) >=20 > 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=20 > 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 precessing with = features of information from flow in packet data. >=20 > 2.6 Anomaly Detection > Proposed by: steniofernandes@gmail.com = > Track: = https://www.ietf.org/mail-archive/web/idnet/current/msg00186.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 precessing with = features of information from flow in packet data. >=20 > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD >=20 > 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. >=20 > 4.2 TBD >=20 > =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 >=20 > > -----Original Message----- > > From: IDNET [mailto:idnet-bounces@ietf.org = ] On Behalf Of Sheng Jiang > > Sent: Wednesday, September 13, 2017 10:20 PM > > To: idnet@ietf.org > > Subject: [Idnet] Applying AI into network management//FW: [nmrg] = 45th > > NMRG > > meeting: Call for Contributions > > > > Hi, IDNet, > > > > After coordinating with NMRG chairs, a Call for Contributions = message > > (see > > below) has been sent by them to the NMRG mailing list regarding to = the > > topic of applying AI into network management. This is in line with = our > > earlier discussion to have a session on this in NMRG, Singapore. You > > could send email to volunteer for presentations in either NMRG or > > IDNet mailing list (I will bridge to NMRG chairs in the IDNet case) = or cross post. > > > > Looking forward for your contributions and good discussion in = Singapore. > > > > Best regards, > > > > Sheng > > > > -----Original Message----- > > From: nmrg [mailto:nmrg-bounces@irtf.org = ] On Behalf Of Lisandro > > Zambenedetti Granville > > Sent: Wednesday, September 13, 2017 9:57 PM > > To: nmrg@irtf.org > > Subject: [nmrg] 45th NMRG meeting: Call for Contributions > > > > Call for Contributions > > 45th NMRG meeting at IETF 100 > > > > In the next IETF100/Singapore we will be organizing the 45th NMRG = meeting. > > We would like to center the upcoming meeting around the use of > > artificial intelligence (AI) for network management, including = related > > topics as diverse as machine-learning and intelligent-defined = networks, for example. > > > > AI for network management is not a new topic, as can be easily > > observed in the literature produced by the network management = community already years ago. > > On the other hand, AI has matured a lot, finding applications is = several areas. > > People interested in the subject also formed communities that can > > contribute too. As such, revisiting AI for network management is not > > only appropriate but also timely. > > > > In this Call for Contributions we would like to receive proposals of > > presentations/discussions for the upcoming 45th NMRG meeting. That > > includes, for example: > > > > - Use cases where AI could/should be used in network management > > - Real-life experiments, results, and findings on AI for network > > management > > - Disruptive and/or new management paradigms based on AI > > - Potential standard requirements for applying AI for network > > management > > - Both preliminary and mature approaches > > > > Please contribute and feel free to distribute this call to other > > mailing lists whose members you believe would be interested and = could contribute too. > > > > Best regards, Lisandro and Laurent. > > _______________________________________________ > > nmrg mailing list > > nmrg@irtf.org > > https://www.irtf.org/mailman/listinfo/nmrg = > > _______________________________________________ > > 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 > _______________________________________________ > 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 --Apple-Mail=_130822D4-91B5-4F0C-B5E7-EBCBE87BA623 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 Hi All

Re: = deep learning survey and tutorial http://web.cse.ohio-state.edu/~panda.2/hoti17-dl.pdf
<= div class=3D"">
You may be = interested in the deep learning survey and tutorial presented recently = at Interconnects =E2=80=992017
=09 =09 =09

Best Regards

Alex



On 20 Sep 2017, at 14:00, = Albert Cabellos <albert.cabellos@gmail.com> wrote:

Hi all

We have been working on using a deep-reinforcement learning = agent to automatically achieve optimal routing configuration. We = demonstrate the efficiency of the agent by means of simulations, we will = soon (2 weeks aprox) make the results public.

The results might = be interesting for the IDNET community at large since, at the best of = our knowledge, this is the first use of deep-reinforcement learning for = route optimization.

In addition to this, the agent uses a = reward function that must be set by the operator. This function = describes in mathematical terms the desired state of the network, for = instance to load-balance traffic among the links. The agent then aims to = maximize the reward function.

This function actually represents the = policy set by the orchestrator. In my honest opinion there is an = interesting discussion on how to express such functions in terms of = management policy, this might be relevant for the NMRG = community.

Albert


On Tue, Sep 19, = 2017 at 4:34 PM, Ciavaglia, Laurent (Nokia - FR/Nozay) <laurent.ciavaglia@nokia-bell-labs.com> = wrote:
Dear Yansen, = all,

We (NMRG chairs) will coordinate with Sheng/IDNET for defining the = agenda.
Please send your proposal to either lists.

Thanks, Laurent.


-----Original Message-----
From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of = yanshen
Sent: Tuesday, September 19, 2017 8:48 AM
To: Sheng Jiang <jiangsheng@huawei.com>; idnet@ietf.org
Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] = 45th NMRG meeting: Call for Contributions

Hi Sheng,

I would like to have a short presentation about the Use case of Traffic = Prediction/QoS Model.

My question is how to "register"? I directly send Email to the NMRG = chair or we have a pre-registration in IDNet ?

I attach the brief summary of use cases in the end. Hope it helpful.

Yansen


=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
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<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.

        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 precessing with features of information from = flow in packet data.

        2.6 Anomaly Detection
                Proposed by: steniofernandes@gmail.com
                Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00186.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 precessing with features of information from = flow in packet data.

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

=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

> -----Original Message-----
> From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of = Sheng Jiang
> Sent: Wednesday, September 13, 2017 10:20 PM
> To: idnet@ietf.org
> Subject: [Idnet] Applying AI into network management//FW: [nmrg] = 45th
> NMRG
> meeting: Call for Contributions
>
> Hi, IDNet,
>
> After coordinating with NMRG chairs, a Call for Contributions = message
> (see
> below) has been sent by them to the NMRG mailing list regarding to = the
> topic of applying AI into network management. This is in line with = our
> earlier discussion to have a session on this in NMRG, Singapore. = You
> could send email to volunteer for presentations in either NMRG = or
> IDNet mailing list (I will bridge to NMRG chairs in the IDNet case) = or cross post.
>
> Looking forward for your contributions and good discussion in = Singapore.
>
> Best regards,
>
> Sheng
>
> -----Original Message-----
> From: nmrg [mailto:nmrg-bounces@irtf.org] On Behalf Of Lisandro
= > Zambenedetti Granville
> Sent: Wednesday, September 13, 2017 9:57 PM
> To: nmrg@irtf.org
> Subject: [nmrg] 45th NMRG meeting: Call for Contributions
>
> Call for Contributions
> 45th NMRG meeting at IETF 100
>
> In the next IETF100/Singapore we will be organizing the 45th NMRG = meeting.
> We would like to center the upcoming meeting around the use of
> artificial intelligence (AI) for network management, including = related
> topics as diverse as machine-learning and intelligent-defined = networks, for example.
>
> AI for network management is not a new topic, as can be easily
> observed in the literature produced by the network management = community already years ago.
> On the other hand, AI has matured a lot, finding applications is = several areas.
> People interested in the subject also formed communities that = can
> contribute too. As such, revisiting AI for network management is = not
> only appropriate but also timely.
>
> In this Call for Contributions we would like to receive proposals = of
> presentations/discussions for the upcoming 45th NMRG meeting. = That
> includes, for example:
>
> - Use cases where AI could/should be used in network management
> - Real-life experiments, results, and findings on AI for network
> management
> - Disruptive and/or new management paradigms based on AI
> - Potential standard requirements for applying AI for network
> management
> - Both preliminary and mature approaches
>
> Please contribute and feel free to distribute this call to other
> mailing lists whose members you believe would be interested and = could contribute too.
>
> Best regards, Lisandro and Laurent.
> _______________________________________________
> nmrg mailing list
> nmrg@irtf.org
> https://www.irtf.org/mailman/listinfo/nmrg
> _______________________________________________
> 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

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

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