Proposed Network Machine Learning Research Group
|Document||Proposed charter||Proposed Network Machine Learning Research Group RG (nmlrg) Snapshot|
|Title||Proposed Network Machine Learning Research Group|
|State||Not currently under review|
|Send notices to||(None)|
Proposed Network Machine Learning Research Group Machine learning technologies can learn from historical data, and make predictions or decisions, rather than following strictly static program instructions. They can dynamically adapt to a changing situation and enhance their own intelligence with by learning from new data. This approach has been successful in image analysis, pattern recognition, language recognition, conversation simulation, and many other applications. It can learn and complete complicated tasks. It also has potential in the network technology area. It can be used to intelligently learn the various environments of networks and react to dynamic situations better than a fixed algorithm. When it becomes mature, it would be greatly accelerate the development of autonomic networking. The Network Machine Learning Research Group (NMLRG) provides a forum for researchers to explore the potential of machine learning technologies for networks. In particular, the NMLRG will work on potential approaches that apply machine learning technologies in network control, network management, and supplying network data for upper-layer applications. The initial focus of the NMLRG will be on higher-layer concepts where the machine learning mechanism could be applied in order to enhance the network establishing, controlling, managing, network applications and customer services. This includes mechanisms to acquire knowledge from the existing networks so that new networks can be established with minimum efforts; the potential to use machine learning mechanisms for routing control and optimization; using machine learning mechanisms in network management to predict future network status; using machine learning mechanisms to autonomic and dynamically manage the network; using machine learning mechanisms to analyze network faults and support recovery; learning network attacks and their behavior, so that protection mechanisms could be self-developed; unifying the data structure and the communication interface between network/network devices and customers, so that the upper-layer applications could easily obtain relevant network information, etc. The NMLRG is expected to identify and document requirements, to survey possible approaches, to provide specifications for proposed solutions, and to prove concepts with prototype implementations that can be tested in real-world environments. The group will report its progress through a publicly accessible web site and presentations at IETF meetings. Specifications developed by the NMLRG will be submitted for publication as Experimental or Informational RFCs. Both academic researchers and researchers from the network industry, including application providers, network operators and vendors, are welcome, as long as they are interested to apply machine learning in network area. Membership Membership is open to any interested parties/individuals. Meetings Regular working meetings are held about two/three times per year at locations convenient to the majority of the participants. Working meetings typically take 1-2 days and are typically co-located with either IETF meetings or conferences related to machine learning or autonomic networks.