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

Dear Laurent,

Thanks for your answer. 

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 <yanshen@huawei.com>; Sheng Jiang <jiangsheng@huawei.com>;
> idnet@ietf.org
> Subject: RE: Applying AI into network management//FW: [nmrg] 45th NMRG
> meeting: Call for Contributions
> 
> 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
> 
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