Re: [Idnet] IDN dedicated session call for case

Jérôme François <jerome.francois@inria.fr> Fri, 11 August 2017 06:54 UTC

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Subject: Re: [Idnet] IDN dedicated session call for case
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Hi,

As you said, there might be several algorithms or techniques to be used
in ML problems.

However, I understand from the first use case description that use case
description should be independent of the ML algorithm as much as possible.
Otherwise, we will mutliply the number of use cases.


jerome

Le 11/08/2017 à 04:32, 김민석 a écrit :
>
> Hi Yansen,
>
>
> Thank you for check my usecase.
>
>
> I know that the usecase is similar topic witht Jerome's one.
>
> However, I'm focusing on creative dataset for ML-based model.
> We already discussed dataset applying of learning process for a
> network architecture in last IETF side meeting, but we lost some
> of points that pre-processing data to apply ML-based learning model is
> needed with much more efforts. Especially, in trendy deep
> learning models such as CNN & RNN, cretive dataset is a significant
> part for efficiently deciding and making system performance. As many
> guys knows, traffic classification using classical ML algorithms such
> as anomaly detection or random decision forest had discussed in last
> NMLRG so that we need more hot trendy issues in aspect of new network
> machine learning.
>
>
> Acually, our team is developing real time deep learning model for
> traffic classification and makes an effort of pre-processing to create
> ml dataset to apply a couple of deep models. In case of CNN, we
> collect features for information of applications in payload, then
> transfer it as like an image[MxN] of dataset. We have another approach
> of pre-processing of RNN that we are collecting specific patterns from
> # of packets per application. We also consider a few different methods
> of ml-based pre-processing for deep learning models in a network
> achitecture.
>
>
> If possible, we should set of a new usecase that how ml-based dataset
> for deep learning models are created by pre-processing in a network
> architecture.
>
>
> Best, 
>
>  
>
> Min-Suk Kim
>  
> Senior Researcher / Ph.D.
>  
>
>  
>
>  
>
> ------------------------------------------------------------------------
> *보낸 사람 : *"yanshen" <yanshen@huawei.com>
> *보낸 날짜 : *2017-08-10 21:40:15 ( +09:00 )
> *받는 사람 : *김민석 <mskim16@etri.re.kr>
> *참조 : *idnet@ietf.org <idnet@ietf.org>rg>, Jérôme François
> <jerome.francois@inria.fr>
> *제목 : *RE: [Idnet] IDN dedicated session call for case
>
>  
>
> Hi Kim,
>
>  
>
> Thanks for your case in advance.
>
>  
>
> BTW, have you ever check the one that Jerome mentioned on Tuesday? It
> is also a traffic classification case.
>
>  
>
> Apologized that I have no more insight in this area. What is the
> difference between these two?
>
>  
>
> At least, whatever, this topic is high focused in current.
>
>  
>
> Yansen
>
>  
>
> *From:*김민석[mailto:mskim16@etri.re.kr]
>
>  
>
> *Sent:* Thursday, August 10, 2017 10:55 AM
>
>  
>
> *To:* Jérôme François <jerome.francois@inria.fr>fr>; Albert Cabellos
> <albert.cabellos@gmail.com>om>; yanshen <yanshen@huawei.com>
>
>  
>
> *Cc:* idnet@ietf.org
>
>  
>
> *Subject:* RE: [Idnet] IDN dedicated session call for case
>
>  
>
> HI,
>
>  
>
> We have an use-case for this:
>
>  
>
> Use case n+4: Real time traffic classfication using deep learning
>
>  
>
> Description: 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 propecessing with features of information from flow in
> packet data.
>
>  
>
> process: 1. collect packet data in real-time, 2. preprocessing
> data-set for deep learning models, 3. Training model using deep
> learning (CNN & RNN), 4. On-line data learning & classifying 5.
> Monitoring and analyzing traffic in the web 
>
>  
>
> Data Format: Time : [Start, End, Unit, Number of Value, Sampling Period]
>
>  
>
>                             Position: [Device ID, Port ID]
>
>  
>
>                             Direction: IN / OUT
>
>  
>
>                             Flow level metric: packet & flow size,
> number of packet(RNN), payload parsing
>
>  
>
>  Message: Request: ask for the data
>
>  
>
>                           Reply: Data
>
>  
>
>                           Notice: For notification or others
>
>  
>
>                           Policy: Control policy
>
>  
>
> Regards,
>
>  
>
> Min-Suk Kim
>
>  
>
> Senior Researcher / Ph.D.
>
>  
>
>  
>
>  
>
>  
>
>  
>
> ------------------------------------------------------------------------
>
> *보낸 사람: *"Jérôme François" <jerome.francois@inria.fr
> <mailto:jerome.francois@inria.fr>>
>
> *보낸 날짜: *2017-08-08 23:49:47 ( +09:00 )
>
> *받는 사람: *Albert Cabellos <albert.cabellos@gmail.com
> <mailto:albert.cabellos@gmail.com>>, yanshen <yanshen@huawei.com
> <mailto:yanshen@huawei.com>>
>
> *참조: *idnet@ietf.org <mailto:idnet@ietf.org><idnet@ietf.org
> <mailto:idnet@ietf.org>>
>
> *제목: *Re: [Idnet] IDN dedicated session call for case
>
>  
>
> 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 à06:52, Albert Cabellos a écrit :
>
>  
>
>     Hi all
>
>      
>
>     Here´s another use-case:
>
>      
>
>     Use case N+2: QoE
>
>      
>
>     style="font-size: 12px;">        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.
>
>      
>
>     style="font-size: 12px;">        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.
>
>      
>
>     style="font-size: 12px;">        Data Format:    Time : [Start,
>     End, Unit, Number of Value, Sampling Period]
>
>      
>
>     style="font-size: 12px;">                                Position:
>     [Device ID, Port ID]
>
>      
>
>     style="font-size: 12px;">                               
>     Direction: IN / OUT
>
>      
>
>     style="font-size: 12px;">                                Low-level
>     metric : SNR, Delay, Jitter, queue-size, etc
>
>      
>
>     style="font-size: 12px;">        Message :       Request: ask for
>     the data
>
>      
>
>     style="font-size: 12px;">                                Reply: Data
>
>      
>
>     style="font-size: 12px;">                                Notice:
>     For notification or others
>
>      
>
>     style="font-size: 12px;">                                Policy:
>     Control policy
>
>      
>
>      
>
>     Kind regards
>
>      
>
>     Albert
>
>      
>
>     On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com
>     <mailto: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 <mailto:IDNET@ietf.org>
>
>          
>
>         https://www.ietf.org/mailman/listinfo/idnet
>
>          
>
>      
>
>      
>
>      
>
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