Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions
Albert Cabellos <albert.cabellos@gmail.com> Thu, 19 October 2017 16:18 UTC
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From: Albert Cabellos <albert.cabellos@gmail.com>
Date: Thu, 19 Oct 2017 18:17:59 +0200
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To: "Ciavaglia, Laurent (Nokia - FR/Nozay)" <laurent.ciavaglia@nokia-bell-labs.com>
Cc: yanshen <yanshen@huawei.com>, Sheng Jiang <jiangsheng@huawei.com>, "idnet@ietf.org" <idnet@ietf.org>
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Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions
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Hi all Below you can find a short report about our work with a Deep-Reinforcement Learning agent that achieves near-optimal routing configurations in one single-step (once trained), automatically and without prior knowledge about the network. https://arxiv.org/pdf/1709.07080.pdf We are in the process of scaling-up the experiments. Kind regards Albert On Wed, Sep 20, 2017 at 3:00 PM, 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 >> >> >> ========================================== >> 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 > >
- [Idnet] Applying AI into network management//FW: … Sheng Jiang
- Re: [Idnet] Applying AI into network management//… yanshen
- Re: [Idnet] Applying AI into network management//… Ciavaglia, Laurent (Nokia - FR/Nozay)
- Re: [Idnet] Applying AI into network management//… yanshen
- Re: [Idnet] Applying AI into network management//… Diego R. Lopez
- Re: [Idnet] Applying AI into network management//… Sheng Jiang
- Re: [Idnet] Applying AI into network management//… Albert Cabellos
- Re: [Idnet] Applying AI into network management//… Alex Galis
- Re: [Idnet] Applying AI into network management//… Sheng Jiang
- Re: [Idnet] Applying AI into network management//… Albert Cabellos
- Re: [Idnet] Applying AI into network management//… stephane.senecal