Re: [Idnet] IDN dedicated session call for case
Stenio Fernandes <sflf@cin.ufpe.br> Tue, 08 August 2017 15:22 UTC
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From: Stenio Fernandes <sflf@cin.ufpe.br>
Date: Tue, 08 Aug 2017 11:20:37 -0400
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To: Jérôme François <jerome.francois@inria.fr>
Cc: "Diego R. Lopez" <diego.r.lopez@telefonica.com>, Albert Cabellos <albert.cabellos@gmail.com>, yanshen <yanshen@huawei.com>, "idnet@ietf.org" <idnet@ietf.org>
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Subject: Re: [Idnet] IDN dedicated session call for case
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Hi Jerome, Diego, et al, Those are excellent use cases. I have some published work on applied machine learning to computer networking problems, including flow-based traffic classification. I think another use case would be applying unsupervised learning techniques for anomaly detection. I can elaborate further on this. Stenio On Tue, Aug 8, 2017 at 10:59 AM, Jérôme François <jerome.francois@inria.fr> wrote: > 100% agree with you. I was far from being exhaustive as traffic features may > depend on types of traffic (kin of sub use cases) > > jerome > > Le 08/08/2017 à 16:56, Diego R. Lopez a écrit : > > Hi Jerome, > > > > Agreed. This is a use case we are very much interested in, and actually > working in it now. Just let me say we are trying to evaluate which are the > significant features of the flow to perform a proper classification, > depending on the flow nature (TLS, DTLS, QUIC, IPsec…), and that would > define the concrete data to be exchanged or stored. > > > > 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 8/8/2017, 16:49 , "IDNET on behalf of Jérôme François" > <idnet-bounces@ietf.org on behalf of jerome.francois@inria.fr> wrote: > > > > Hi all, > > Here is another use case about traffic classification. > > Use case N+3: (encrypted) traffic classification > > Description: 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 trafic with > the underlying application assuming that the granularity of classification > may vary (type of application, exact application name, version...) > Process: 1. collect packet information 2. flow reassembly (using > directly flow format such as IPFIX might be possible but depends on the type > of traffic, e.g. extracting the TLS application data is useful for encrypted > traffic) 3. Collect application specific information (useful when targeting > a single type of application) = out of network information 4. train the > model 5. Online or offline testing 4. Apply application level policies. > Data Format: Time : [Start, End, Unit, Number of Value, Sampling > Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Flow level metric: packet size > distributions, number of packets, inter-arrival time distribution, > (+ application specific knowledge : payload > parsing) > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > Best regards, > jerome > > > Le 08/08/2017 à 06:52, Albert Cabellos a écrit : > > Hi all > > > > Here´s another use-case: > > > > Use case N+2: QoE > Description: 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. > Process: 1. Low-level data collection and QoE measurement ; 2. > Training Model (input low-level metrics, output QoE); 3. Real-time data > capture and input; 4. Predict QoE; 5. Operate network to meet target QoE > requirement, go to 3. > Data Format: Time : [Start, End, Unit, Number of Value, Sampling > Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Low-level metric : SNR, Delay, Jitter, > queue-size, etc > > > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > > > Kind regards > > > > Albert > > > > On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote: > > Dear all, > > Since we plan to organize a dedicated session in NMRG, IETF100, for applying > AI into network management (NM), I’d try to list some Use Cases and propose > a roadmap and ToC before Nov. > > These might be rough. You are welcome to refine them and propose your > focused use cases or ideas. > > Use case 1: Traffic Prediction > Description: 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. > Process: 1. Data collection (e.g. traffic sample of physical/logical > port ); 2. Training Model; 3. Real-time data capture and input; 4. > Predication output; 5. Fix error and go back to 3. > Data Format: Time : [Start, End, Unit, Number of Value, Sampling > Period] > Position: [Device ID, Port ID] > Direction: IN / OUT > Route : [R1, R2, ..., RN] (might be useful > for some scenarios) > Service : [Service ID, Priority, ...] (Not > clear how to use it but seems useful) > Traffic: [T0, T1, T2, ..., TN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case 2: QoS Management > Description: Use multiple paths to distribute the traffic flows. > Adjust the percentages. Avoid congestion and ensure QoS. > Process: 1. Data capture (e.g. traffic sample of physical/logical > port ); 2. Training Model; 3. Real-time data capture and input; 4. Output > percentages; 5. Fix error and go back to 3. > Data Format: Time : [Timestamp, Value type (Delay/Packet > Loss/...), Unit, Number of Value, Sampling Period] > Position: [Link ID, Device ID] > Value: [V0, V1, V2, ..., VN] > Message : Request: ask for the data > Reply: Data > Notice: For notification or others > Policy: Control policy > > Use case N: Waiting for your Ideas > > Also I suggest a roadmap before Nov if possible. > > ### Roadmap ### > Aug. : Collecting the use cases (related with NM). Rough thoughts and > requirements > Sep. : Refining the cases and abstract the common elements > Oct. : Deeply analysis. Especially on Data Format, control flow, or other > key points > Nov.: F2F discussions on IETF100 > ### Roadmap End ### > > A rough ToC is listed in following. We may take it as a scope before Nov. > Hope that the content could become the draft of draft. > > ###Table of Content### > 1. Gap and Requirement Analysis > 1.1 Network Management requirement > 1.2 TBD > 2. Use Cases > 2.1 Traffic Prediction > 2.2 QoS Management > 3.3 TBD > 3. Data Focus > 3.1 Data attribute > 3.2 Data format > 3.3 TBD > 4. Aims > 4.1 Benchmarking Framework > 4.2 TBD > ###ToC End### > > > Yansen > > _______________________________________________ > 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 > > > > > ________________________________ > > Este mensaje y sus adjuntos se dirigen exclusivamente a su destinatario, > puede contener información privilegiada o confidencial y es para uso > exclusivo de la persona o entidad de destino. Si no es usted. el > destinatario indicado, queda notificado de que la lectura, utilización, > divulgación y/o copia sin autorización puede estar prohibida en virtud de la > legislación vigente. Si ha recibido este mensaje por error, le rogamos que > nos lo comunique inmediatamente por esta misma vía y proceda a su > destrucción. > > The information contained in this transmission is privileged and > confidential information intended only for the use of the individual or > entity named above. If the reader of this message is not the intended > recipient, you are hereby notified that any dissemination, distribution or > copying of this communication is strictly prohibited. If you have received > this transmission in error, do not read it. Please immediately reply to the > sender that you have received this communication in error and then delete > it. > > Esta mensagem e seus anexos se dirigem exclusivamente ao seu destinatário, > pode conter informação privilegiada ou confidencial e é para uso exclusivo > da pessoa ou entidade de destino. Se não é vossa senhoria o destinatário > indicado, fica notificado de que a leitura, utilização, divulgação e/ou > cópia sem autorização pode estar proibida em virtude da legislação vigente. > Se recebeu esta mensagem por erro, rogamos-lhe que nos o comunique > imediatamente por esta mesma via e proceda a sua destruição > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > -- Prof. Stenio Fernandes CIn/UFPE http://www.steniofernandes.com
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