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

Sheng Jiang <jiangsheng@huawei.com> Wed, 20 September 2017 03:29 UTC

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From: Sheng Jiang <jiangsheng@huawei.com>
To: "Diego R. Lopez" <diego.r.lopez@telefonica.com>, "Ciavaglia, Laurent (Nokia - FR/Nozay)" <laurent.ciavaglia@nokia-bell-labs.com>, yanshen <yanshen@huawei.com>, "idnet@ietf.org" <idnet@ietf.org>
Thread-Topic: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions
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Date: Wed, 20 Sep 2017 03:28:52 +0000
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Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions
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Thanks, Diego & Shen, for your volunteer & contribution. Your presentations have been counted in. We are waiting for more volunteers & presentations. Hopefully, we would be able to work out a agenda before the end of this month.

Best regards,

Sheng

> -----Original Message-----
> From: Diego R. Lopez [mailto:diego.r.lopez@telefonica.com]
> Sent: Wednesday, September 20, 2017 3:06 AM
> To: Ciavaglia, Laurent (Nokia - FR/Nozay); yanshen; Sheng Jiang;
> idnet@ietf.org
> Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg]
> 45th NMRG meeting: Call for Contributions
> 
> Hi,
> 
> I'd like to propose a presentation on the results of the COGNET project,
> including the double closed loop we have been devising as main
> architecture, and the results on dataset generation.
> 
> Be goode,
> 
> --
> "Esta vez no fallaremos, Doctor Infierno"
> 
> Dr Diego R. Lopez
> Telefonica I+D
> http://people.tid.es/diego.lopez/
> 
> e-mail: diego.r.lopez@telefonica.com
> Tel:    +34 913 129 041
> Mobile: +34 682 051 091
> ----------------------------------
> 
> 
> On 19/9/2017, 09:34 , "IDNET on behalf of Ciavaglia, Laurent (Nokia -
> FR/Nozay)" <idnet-bounces@ietf.org on behalf of
> 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
> 
> 
>     ==========================================
>     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
> 
>     =========================================
> 
>     > -----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
> 
> 
> 
> ________________________________
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