Re: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG meeting: Call for Contributions

[yanshen <[e-mail]>]

Hi Sheng, 

I would like to have a short presentation about the Use case of Traffic Prediction/QoS Model. 

My question is how to "register"? I directly send Email to the NMRG chair or we have a pre-registration in IDNet ?

I attach the brief summary of use cases in the end. Hope it helpful. 

Yansen


==========================================
1. Gap and Requirement Analysis
    1.1 Network Management requirement
    1.2 TBD
2. Use Cases
    2.1 Traffic Prediction
		Proposed by: [e-mail]
		Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html
		Abstract: 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.
		
    2.2 QoS Management
		Proposed by: [e-mail]
		Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00131.html
		Abstract: Use multiple paths to distribute the traffic flows. Adjust the percentages. Avoid congestion and ensure QoS. 
	
    2.3 Application (and/or DDoS) detection
		Proposed by: [e-mail]
		Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00133.html
		Abstract: Detect the application (or attack) from network packets (HTTPS or plain) Collect the history traffic data and identify a service or attack (ex: Skype, Viber, DDoS attack etc.)
	
	2.4 QoE Management
		Proposed by: [e-mail]
		Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00137.html
		Abstract: 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.
	
	2.5 (Encrypted) Traffic Classification
		Proposed by: [e-mail]; [e-mail]
		Track: [Jerome] https://www.ietf.org/mail-archive/web/idnet/current/msg00141.html ; [Min-Suk Kim] https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html
		Abstract: 
			[Jerome] 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 traffic with the underlying application assuming that the granularity of classification may vary (type of application, exact application name, version...)
			[Min-Suk Kim]continuously collect packet data, then applying learning process for traffic classification with generating application using deep learning models such as CNN (convolutional neural network) and RNN (recurrent neural network). Data-set to apply into the models are generated by precessing with features of information from flow in packet data.
		
	2.6 Anomaly Detection
		Proposed by: [e-mail]
		Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00186.html
		Abstract: 
			[Jerome] 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 traffic with the underlying application assuming that the granularity of classification may vary (type of application, exact application name, version...)
			[Min-Suk Kim]continuously collect packet data, then applying learning process for traffic classification with generating application using deep learning models such as CNN (convolutional neural network) and RNN (recurrent neural network). Data-set to apply into the models are generated by precessing with features of information from flow in packet data.
	
3. Data Focus
    3.1 Data attribute
    3.2 Data format
    3.3 TBD
	
4. Support Technologies
    4.1 Benchmarking Framework
		Proposed by: [e-mail]
		Track: https://www.ietf.org/mail-archive/web/idnet/current/msg00146.html
		Abstract: A proper benchmarking framework comprises a set of reference procedures, methods, and models that can (or better *must*) be followed to assess the quality of an AI mechanism proposed to be applied to the network management/control area. Moreover, and much more specific to the IDNET topics, is the inclusion, dependency, or just the general relation of a standard format enforced to the data that is used (input) and produced (output) by the framework, so a kind of "data market" can arise without requiring to transform the data. The initial scope of input/output data would be the datasets, but also the new knowledge items that are stated as a result of applying the benchmarking procedures defined by the framework, which can be collected together to build a database of benchmark results, or just contrasted with other existing entries in the database to know the position of the solution just evaluated. This increases the usefulness of IDNET.
	
    4.2 TBD

=========================================

> -----Original Message-----
> From: IDNET [mailto:[e-mail]] On Behalf Of Sheng Jiang
> Sent: Wednesday, September 13, 2017 10:20 PM
> To: [e-mail]
> Subject: [Idnet] Applying AI into network management//FW: [nmrg] 45th NMRG
> meeting: Call for Contributions
> 
> Hi, IDNet,
> 
> After coordinating with NMRG chairs, a Call for Contributions message (see
> below) has been sent by them to the NMRG mailing list regarding to the topic
> of applying AI into network management. This is in line with our earlier
> discussion to have a session on this in NMRG, Singapore. You could send email
> to volunteer for presentations in either NMRG or IDNet mailing list (I will bridge
> to NMRG chairs in the IDNet case) or cross post.
> 
> Looking forward for your contributions and good discussion in Singapore.
> 
> Best regards,
> 
> Sheng
> 
> -----Original Message-----
> From: nmrg [mailto:[e-mail]] On Behalf Of Lisandro
> Zambenedetti Granville
> Sent: Wednesday, September 13, 2017 9:57 PM
> To: [e-mail]
> Subject: [nmrg] 45th NMRG meeting: Call for Contributions
> 
> Call for Contributions
> 45th NMRG meeting at IETF 100
> 
> In the next IETF100/Singapore we will be organizing the 45th NMRG meeting.
> We would like to center the upcoming meeting around the use of artificial
> intelligence (AI) for network management, including related topics as diverse
> as machine-learning and intelligent-defined networks, for example.
> 
> AI for network management is not a new topic, as can be easily observed in the
> literature produced by the network management community already years ago.
> On the other hand, AI has matured a lot, finding applications is several areas.
> People interested in the subject also formed communities that can contribute
> too. As such, revisiting AI for network management is not only appropriate but
> also timely.
> 
> In this Call for Contributions we would like to receive proposals of
> presentations/discussions for the upcoming 45th NMRG meeting. That includes,
> for example:
> 
> - Use cases where AI could/should be used in network management
> - Real-life experiments, results, and findings on AI for network management
> - Disruptive and/or new management paradigms based on AI
> - Potential standard requirements for applying AI for network management
> - Both preliminary and mature approaches
> 
> Please contribute and feel free to distribute this call to other mailing lists
> whose members you believe would be interested and could contribute too.
> 
> Best regards, Lisandro and Laurent.
> _______________________________________________
> nmrg mailing list
> [e-mail]
> https://www.irtf.org/mailman/listinfo/nmrg
> _______________________________________________
> IDNET mailing list
> [e-mail]
> https://www.ietf.org/mailman/listinfo/idnet