Re: [Idnet] IDN dedicated session call for case

Hi IDneter, especially the contributors, 

(copied to NMRG mail list for coordination between two groups)

It is our intention to organize a dedicated IDNet session for applying AI into network management in NMRG in IETF100, Singapore. It will depend on whether we could converge and organize good content/presentations. So far, we are actually in the stage of calling interests, use cases and contributors. We need to keep discussing and converge to specific use cases soon so that we could form a good proposal for the potential session.

Shen's work has given a constructive thought that may lead to converged content. It is welcome to everybody contribute more valuable user cases. After refining and abstracting the general element from various use cases, the potential standard points will become more and more clear. And maybe, after several round of discussion and convergence, we could understand the essential of applying AI into network management more so that we could have a good foundation to form an IETF working group for standardization work.

Regards,

Sheng

> -----Original Message-----
> From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of yanshen
> Sent: Wednesday, August 02, 2017 6:12 PM
> To: idnet@ietf.org
> Subject: [Idnet] IDN dedicated session call for case
> 
> 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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