Network Machine Learning
draft-jiang-nmlrg-network-machine-learning-02

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Network Machine Learning Research Group                         S. Jiang
Internet-Draft                              Huawei Technologies Co., Ltd
Intended status: Informational                          October 28, 2016
Expires: May 1, 2017

                        Network Machine Learning
             draft-jiang-nmlrg-network-machine-learning-02

Abstract

   This document introduces background information of machine learning
   briefly, then explores the potential of machine learning techniques
   for networks.  This document is serving as a white paper of the
   (proposed) IRTF Network Machine Learning Research Group.

Status of This Memo

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   This Internet-Draft will expire on May 1, 2017.

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   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Jiang                      Expires May 1, 2017                  [Page 1]
Internet-Draft          Network Machine Learning            October 2016

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Brief Background of Machine Learning  . . . . . . . . . . . .   3
     3.1.  Machine Learning Categories . . . . . . . . . . . . . . .   3
     3.2.  Machine Learning Approaches . . . . . . . . . . . . . . .   3
     3.3.  Successful Applications . . . . . . . . . . . . . . . . .   5
     3.4.  Precondition of Applying Machine Learning Approach  . . .   5
     3.5.  Limitation of Machine Learning Mechanism  . . . . . . . .   5
   4.  Network Machine Learning Research Group in IRTF . . . . . . .   6
   5.  Use Cases Study of Applying Machine Learning in Network . . .   7
     5.1.  Network Traffic . . . . . . . . . . . . . . . . . . . . .   7
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   8
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   8
   9.  Change log [RFC Editor: Please remove]  . . . . . . . . . . .   8
   10. Informative References  . . . . . . . . . . . . . . . . . . .   8
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   Machine learning techniques help to make predictions or decisions by
   learning from historical data.  As machine learning mechanism could
   dynamically adapt to varying situations and enhance their own
   intelligence by learning from new data, they are more flexible in
   handling complicated tasks than strictly static program instructions.
   Therefore, machine learning techniques have been widely applied in
   image analysis, pattern recognition, language recognition,
   conversation simulation, and etc.

   With deep exploration, machine learning techniques would cast light
   on studies of autonomic networking, in that they could be well
   adapted to learn the various environments of networks and react to
   dynamic situations.

   The proposed Network Machine Learning Research Group (NMLRG) was
   formed within IRTF (Internet Research Task Force), October, 2015.  As
   a procedure, currently, IRTF requests an one-year provisional period.
   After this period, the proposed research group may become a formal
   research group if there is a steady research community.  The NMLRG
   provides a forum for researchers to explore the potential of machine
   learning techniques for networks.

   This document firstly provides background information of machine
   learning briefly, then explores the potential of machine learning
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