Subject: [nmrg] nmrg-research-challenges-in-artificial-intelligence-for-network-management

From: <[email-masked]>
Date: Thu, 24 Mar 2022 10:23:51 +0000

Dear Jerome,

I have read slides-113-nmrg-research-challenges-in-artificial-intelligence-for-network-management-00.docx and like to comment from an network operators point of view who is developing AI/ML based Network applications.

First of all thank you very much for the good work. The document is easy readable and reflects what we have experienced so far in the development process.

You describe in section 4.1 "Data for AI-based NM solutions", "the first problem to address is to define the data to be collected which will be appropriate to obtain accurate results"

In section 2 " Difficult problems in network management" you mention "C4. Data-dependent solutions. To solve accurately a problem, it can be necessary to rely on data with all subsequent problems (collection and heterogeneity of data, large volume)"

In my opinion C4 should be changed to reflect the following three challenges:

  *   Unable to obtain the necessary operational data from the network because either there are no IETF standards being specified yet to export or software/hardware has not such capabilities
  *   Unable to correlate data due to missing key fields among operational data models because either there are no IETF standards being specified yet to export or software/hardware has not such capabilities
  *   Unable to aggregate and compress data at data export or data collection preventing data being processed in real-time for network analytics

Some background. Due to resource constraints on network nodes, network protocols have been developed in mind to ensure that data is only exposed when needed. Best example is BGP, where only best path is exposed. Or only few protocols have been built to enable debugging. Giving insight how the network protocol is processing the data. For instance IPFIX for packet forwarding. BMP for BGP.

I hope that makes sense.

With best wishes
Thomas