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Proposed Network Machine Learning Research Group (nmlrg) (RG)

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Charter (as of 2015-08-19):

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.