Re: [Idnet] IDN dedicated session call for case

[Özgü Alay <[e-mail]>]

Dear Albert,
We are interested in this use case and will support the activities in this
area.
Best Regards,
Özgü

On 8 August 2017 at 06:52, Albert Cabellos <[e-mail]>
wrote:

> 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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>>
>
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