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

From: "dingxiaojian (A)" <[email-masked]>
Date: Fri, 11 Aug 2017 01:39:06 +0000

Hi Kim,
     I think deep learning method is very hot today, and could be used in solving network problems.
     First, let’s recap what is the essence of deep learning method, what kind of scenario it best fit in?  The best implementation of deep learning is perform image recognition. It take raw image as the input of deep learning model (eg. CNN), and select the reconstructive features to do image recognition. It should be noted that the feature number of raw image may very large, for example, for an image of 1000X1000 resolution ratio, the number of feature is 10^6. I think the main effect of deep learning is feature engineering.
    Another  the well-known use case is Alpha Go. In this use case, the checkerboard is considered as an image. ‘Go’ is more complicated, because it combines several machine learning technologies (eg. Logistic Regression, Reinforcement Learning, deep learning, Monte Carlo tree search)to train the model.
     Let's go back to this use-case n+4.  Can you describe in further that real time traffic data (feature and label)? Is it really need to use the deep learning method in this scenario, or simple machine learning technologies (eg. SVM, BP) can be replaced?

Kind regards,
Xiaojian

发件人: IDNET [mailto:[email-masked]] 代表 ???
发送时间: 2017年8月10日 10:55
收件人: Jérôme François <[email-masked]>; Albert Cabellos <[email-masked]>; yanshen <[email-masked]>
抄送: [email-masked]
主题: 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" <[email-masked]<mailto:[email-masked]>>
보낸 날짜 : 2017-08-08 23:49:47 ( +09:00 )
받는 사람 : Albert Cabellos <[email-masked]<mailto:[email-masked]>>, yanshen <[email-masked]<mailto:[email-masked]>>
참조 : [email-masked]<mailto:[email-masked]> <[email-masked]<mailto:[email-masked]>>
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 à 06:52, 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 <[email-masked]<mailto:[email-masked]>> 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

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