Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions

[Albert Cabellos <[e-mail]>]

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) <
[e-mail]> 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:[e-mail]] On Behalf Of yanshen
> Sent: Tuesday, September 19, 2017 8:48 AM
> To: Sheng Jiang <[e-mail]>; [e-mail]
> 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: [e-mail]
>                 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: [e-mail]
>                 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: [e-mail]
>                 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: [e-mail]
>                 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: [e-mail]; [e-mail]
>                 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: [e-mail]
>                 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: [e-mail]
>                 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:[e-mail]] On Behalf Of Sheng Jiang
> > Sent: Wednesday, September 13, 2017 10:20 PM
> > To: [e-mail]
> > 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:[e-mail]] On Behalf Of Lisandro
> > Zambenedetti Granville
> > Sent: Wednesday, September 13, 2017 9:57 PM
> > To: [e-mail]
> > 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
> > [e-mail]
> > https://www.irtf.org/mailman/listinfo/nmrg
> > _______________________________________________
> > IDNET mailing list
> > [e-mail]
> > https://www.ietf.org/mailman/listinfo/idnet
>
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