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AMCzsaW8WrnAMLtA3huiHv39gHIXgMS+QmkS9HdmeFROzYADOd921Sl0 Q5QpLmo8TBFzMViK9BIti1f9qz+m5VmgAFCl0TI= X-Google-Smtp-Source: ABhQp+QpB90Q7jPNvdDP9Ef0fv++1X3RXAGKF4xBIL+W1bBwhliae8I6OXzOj7Rpx3JdVFTUr0ljn8rimb0FN3AJXks= X-Received: by 10.37.201.193 with SMTP id z184mr1308756ybf.468.1508429880144; Thu, 19 Oct 2017 09:18:00 -0700 (PDT) MIME-Version: 1.0 Received: by 10.37.57.10 with HTTP; Thu, 19 Oct 2017 09:17:59 -0700 (PDT) In-Reply-To: References: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D2999C19@DGGEMM505-MBX.china.huawei.com> From: Albert Cabellos Date: Thu, 19 Oct 2017 18:17:59 +0200 Message-ID: To: "Ciavaglia, Laurent (Nokia - FR/Nozay)" Cc: yanshen , Sheng Jiang , "idnet@ietf.org" Content-Type: text/plain; charset="UTF-8" Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 19 Oct 2017 16:18:10 -0000 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 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) > 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 ; 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> 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 > > From nobody Mon Oct 23 04:54:53 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B22DA13F3C1 for ; Mon, 23 Oct 2017 04:54:51 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.619 X-Spam-Level: X-Spam-Status: No, score=-2.619 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, FREEMAIL_FROM=0.001, RCVD_IN_DNSWL_LOW=-0.7, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001, UNPARSEABLE_RELAY=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 7M1MUxBupZhj for ; Mon, 23 Oct 2017 04:54:49 -0700 (PDT) Received: from relais-inet.orange.com (mta241.mail.business.static.orange.com [80.12.66.41]) (using TLSv1.2 with cipher AECDH-AES256-SHA (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 6311E13F3A5 for ; Mon, 23 Oct 2017 04:54:49 -0700 (PDT) Received: from opfedar04.francetelecom.fr (unknown [xx.xx.xx.6]) by opfedar22.francetelecom.fr (ESMTP service) with ESMTP id 1F46F607C6; Mon, 23 Oct 2017 13:54:48 +0200 (CEST) Received: from Exchangemail-eme2.itn.ftgroup (unknown [xx.xx.31.61]) by opfedar04.francetelecom.fr (ESMTP service) with ESMTP id F05BF40068; Mon, 23 Oct 2017 13:54:47 +0200 (CEST) Received: from OPEXCLILM43.corporate.adroot.infra.ftgroup ([fe80::ec23:902:c31f:731c]) by OPEXCLILM7E.corporate.adroot.infra.ftgroup ([fe80::b91c:ea2c:ac8a:7462%19]) with mapi id 14.03.0361.001; Mon, 23 Oct 2017 13:54:47 +0200 From: To: Albert Cabellos CC: yanshen , "idnet@ietf.org" , "Sheng Jiang" , "Ciavaglia, Laurent (Nokia - FR/Nozay)" Thread-Topic: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions Thread-Index: AdMsmrntJhqPpIrCTTyzoU5Pp27WrAEdz9xgAAHVMtAAOZc5AAW5WW+AAMP/+WA= Date: Mon, 23 Oct 2017 11:54:47 +0000 Message-ID: <21133_1508759688_59EDD888_21133_365_1_852992ACEDF600469971A6DFB84266522BD291BA@OPEXCLILM43.corporate.adroot.infra.ftgroup> References: <5D36713D8A4E7348A7E10DF7437A4B927CE8D980@NKGEML515-MBX.china.huawei.com> <6AE399511121AB42A34ACEF7BF25B4D2999C19@DGGEMM505-MBX.china.huawei.com> In-Reply-To: Accept-Language: fr-FR, en-US Content-Language: fr-FR X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.168.234.4] Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 23 Oct 2017 11:54:52 -0000 Hi Albert, All, Thank you for pointing out this interesting work. A related work, which might be of interest to you and your colleagues, is a= bout policy gradient approach (reinforcement learning) for network routing:= =20 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=3D10.1.1.20.9394 Kind regards, Stephane=20=20=20 -----Message d'origine----- De=A0: IDNET [mailto:idnet-bounces@ietf.org] De la part de Albert Cabellos Envoy=E9=A0: jeudi 19 octobre 2017 18:18 =C0=A0: Ciavaglia, Laurent (Nokia - FR/Nozay) Cc=A0: yanshen; idnet@ietf.org; Sheng Jiang Objet=A0: Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th = NMRG meeting: Call for Contributions 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 sin= gle-step (once trained), automatically and without prior knowledge about th= e 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 wrote: > Hi all > > We have been working on using a deep-reinforcement learning agent to=20 > automatically achieve optimal routing configuration. We demonstrate=20 > the efficiency of the agent by means of simulations, we will soon (2=20 > weeks > aprox) make the results public. > > The results might be interesting for the IDNET community at large=20 > since, at the best of our knowledge, this is the first use of=20 > deep-reinforcement learning for route optimization. > > In addition to this, the agent uses a reward function that must be set=20 > by the operator. This function describes in mathematical terms the=20 > 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.=20 > In my honest opinion there is an interesting discussion on how to=20 > express such functions in terms of management policy, this might be=20 > relevant for the NMRG community. > > Albert > > > On Tue, Sep 19, 2017 at 4:34 PM, Ciavaglia, Laurent (Nokia - FR/Nozay)=20 > wrote: >> >> Dear Yansen, all, >> >> We (NMRG chairs) will coordinate with Sheng/IDNET for defining the agend= a. >> 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 ; idnet@ietf.org >> Subject: Re: [Idnet] Applying AI into network management//FW: [nmrg]=20 >> 45th NMRG meeting: Call for Contributions >> >> Hi Sheng, >> >> I would like to have a short presentation about the Use case of=20 >> Traffic Prediction/QoS Model. >> >> My question is how to "register"? I directly send Email to the NMRG=20 >> chair or we have a pre-registration in IDNet ? >> >> I attach the brief summary of use cases in the end. Hope it helpful. >> >> Yansen >> >> >> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >> 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=20 >> external data which may influence the traffic. Predict the traffic in=20 >> 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=20 >> 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=20 >> network packets (HTTPS or plain) Collect the history traffic data and=20 >> 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,=20 >> jitter, losses, etc) and measure QoE. Then use ML to understand what=20 >> is the relation between satisfactory QoE and the low-level metrics.=20 >> As an example learn that when delay>N then QoE is degraded, but when=20 >> M> that QoE cannot be measured directly over your network). This is=20 >> useful to understand how the network must be operated to provide satisfa= ctory 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 ;=20 >> [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg001= 53.html >> Abstract: >> [Jerome] collect flow-level traffic metrics=20 >> such as protocol information but also meta metrics such as=20 >> distribution of packet sizes, inter-arrival times... Then use such=20 >> information to label the traffic with the underlying application=20 >> assuming that the granularity of classification may vary (type of=20 >> application, exact application name, >> version...) >> [Min-Suk Kim]continuously collect packet=20 >> data, then applying learning process for traffic classification with=20 >> generating application using deep learning models such as CNN=20 >> (convolutional neural >> network) and RNN (recurrent neural network). Data-set to apply into=20 >> the models are generated by precessing with features of information=20 >> 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=20 >> such as protocol information but also meta metrics such as=20 >> distribution of packet sizes, inter-arrival times... Then use such=20 >> information to label the traffic with the underlying application=20 >> assuming that the granularity of classification may vary (type of=20 >> application, exact application name, >> version...) >> [Min-Suk Kim]continuously collect packet=20 >> data, then applying learning process for traffic classification with=20 >> generating application using deep learning models such as CNN=20 >> (convolutional neural >> network) and RNN (recurrent neural network). Data-set to apply into=20 >> the models are generated by precessing with features of information=20 >> 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=20 >> set of reference procedures, methods, and models that can (or better=20 >> *must*) be followed to assess the quality of an AI mechanism proposed=20 >> to be applied to the network management/control area. Moreover, and=20 >> much more specific to the IDNET topics, is the inclusion, dependency,=20 >> or just the general relation of a standard format enforced to the=20 >> data that is used (input) and produced >> (output) by the framework, so a kind of "data market" can arise=20 >> without requiring to transform the data. The initial scope of=20 >> input/output data would be the datasets, but also the new knowledge=20 >> items that are stated as a result of applying the benchmarking=20 >> procedures defined by the framework, which can be collected together=20 >> to build a database of benchmark results, or just contrasted with=20 >> other existing entries in the database to know the position of the=20 >> solution just evaluated. This increases the usefulness of IDNET. >> >> 4.2 TBD >> >> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >> >> > -----Original Message----- >> > From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Sheng=20 >> > Jiang >> > Sent: Wednesday, September 13, 2017 10:20 PM >> > To: idnet@ietf.org >> > Subject: [Idnet] Applying AI into network management//FW: [nmrg]=20 >> > 45th NMRG >> > meeting: Call for Contributions >> > >> > Hi, IDNet, >> > >> > After coordinating with NMRG chairs, a Call for Contributions=20 >> > message (see >> > below) has been sent by them to the NMRG mailing list regarding to=20 >> > the topic of applying AI into network management. This is in line=20 >> > with our earlier discussion to have a session on this in NMRG,=20 >> > Singapore. You could send email to volunteer for presentations in=20 >> > either NMRG or IDNet mailing list (I will bridge to NMRG chairs in=20 >> > the IDNet case) or cross post. >> > >> > Looking forward for your contributions and good discussion in Singapor= e. >> > >> > Best regards, >> > >> > Sheng >> > >> > -----Original Message----- >> > From: nmrg [mailto:nmrg-bounces@irtf.org] On Behalf Of Lisandro=20 >> > 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=20 >> > meeting. >> > We would like to center the upcoming meeting around the use of=20 >> > artificial intelligence (AI) for network management, including=20 >> > related topics as diverse as machine-learning and=20 >> > intelligent-defined networks, for example. >> > >> > AI for network management is not a new topic, as can be easily=20 >> > observed in the literature produced by the network management=20 >> > community already years ago. >> > On the other hand, AI has matured a lot, finding applications is=20 >> > several areas. >> > People interested in the subject also formed communities that can=20 >> > contribute too. As such, revisiting AI for network management is=20 >> > not only appropriate but also timely. >> > >> > In this Call for Contributions we would like to receive proposals=20 >> > of presentations/discussions for the upcoming 45th NMRG meeting.=20 >> > 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=20 >> > management >> > - Disruptive and/or new management paradigms based on AI >> > - Potential standard requirements for applying AI for network=20 >> > management >> > - Both preliminary and mature approaches >> > >> > Please contribute and feel free to distribute this call to other=20 >> > mailing lists whose members you believe would be interested and=20 >> > 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 mailing list IDNET@ietf.org https://www.ietf.org/mailman/listinfo/idnet ___________________________________________________________________________= ______________________________________________ Ce message et ses pieces jointes peuvent contenir des informations confiden= tielles ou privilegiees et ne doivent donc pas etre diffuses, exploites ou copies sans autorisation. 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