Re: [Idnet] IDN dedicated session call for case

[yanshen <[e-mail]>]

Hi Stenio,

Recently I also think about that use un/supervised ML method to implement the routing process. 

May I suggest you to have a simple refining and push it into mail list ? Just roughly refer to the format.

It will be helpful for both the discussion and your future work. 

Yansen

> -----Original Message-----
> From: [e-mail] [mailto:[e-mail]] On
> Behalf Of Stenio Fernandes
> Sent: Tuesday, August 08, 2017 11:21 PM
> To: Jérôme François <[e-mail]>
> Cc: Diego R. Lopez <[e-mail]>; Albert Cabellos
> <[e-mail]>; yanshen <[e-mail]>; [e-mail]
> Subject: Re: [Idnet] IDN dedicated session call for case
> 
> 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 <[e-mail]>
> 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: [e-mail]
> >
> > Tel:        [phone]
> >
> > Mobile: [phone]
> >
> > -----------------------------------
> >
> >
> >
> > On 8/8/2017, 16:49 , "IDNET on behalf of Jérôme François"
> > <[e-mail] on behalf of [e-mail]> 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 <[e-mail]> 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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> > https://www.ietf.org/mailman/listinfo/idnet
> >
> >
> >
> >
> >
> >
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> >
> >
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> 
> 
> --
> Prof. Stenio Fernandes
> CIn/UFPE
> http://www.steniofernandes.com