Re: [Idnet] IDN dedicated session call for case

[Stenio Fernandes <[e-mail]>]

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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-- 
Prof. Stenio Fernandes
CIn/UFPE
http://www.steniofernandes.com