Re: [Idnet] IDN dedicated session call for case

Albert Cabellos <albert.cabellos@gmail.com> Tue, 08 August 2017 04:52 UTC

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From: Albert Cabellos <albert.cabellos@gmail.com>
Date: Tue, 8 Aug 2017 13:52:23 +0900
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To: yanshen <yanshen@huawei.com>
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Subject: Re: [Idnet] IDN dedicated session call for case
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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 <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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