Re: [Idnet] IDN dedicated session call for case

김민석 <mskim16@etri.re.kr> Thu, 10 August 2017 02:55 UTC

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Thread-Topic: [Idnet] IDN dedicated session call for case
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Subject: Re: [Idnet] IDN dedicated session call for case
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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
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                          Policy: Control policy


Regards,



Min-Suk Kim

Senior Researcher / Ph.D.










________________________________
보낸 사람 : "Jérôme François" <jerome.francois@inria.fr>;
보낸 날짜 : 2017-08-08 23:49:47 ( +09:00 )
받는 사람 : Albert Cabellos <albert.cabellos@gmail.com>;, yanshen <yanshen@huawei.com>;
참조 : idnet@ietf.org <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 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 <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





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