Intelligent Network Management using Reinforcement Learning
draft-kim-nmrg-rl-03

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Network Management Research Group                               M-S. Kim
Internet-Draft                                                 Y-G. Hong
Intended status: Informational                                      ETRI
Expires: January 3, 2019                                        Y-H. Han
                                                               KoreaTech
                                                                T-J. Ahn
                                                                      KT
                                                                K-H. Kim
                                                                    ETRI
                                                            July 2, 2018

      Intelligent Network Management using Reinforcement Learning
                          draft-kim-nmrg-rl-03

Abstract

   This document describes intelligent network management system to
   autonomously manage and monitor using machine learning techniques.
   Reinforcement learning is one of the machine learning techniques that
   can provide autonomously management with multi-agent path-planning
   over a communication network.  According to intelligent distributed
   multi-agent system, the main centralized node called by the global
   environment should not only manage all agents workflow in a hybrid
   peer-to-peer networking architecture and, but transfer and share
   information in distributed nodes.  All agents in distributed nodes
   are able to be provided with a cumulative reward for each action that
   a given agent takes with respect to an optimized knowledge based on a
   to-be-learned policy over the learning process.  The optimized and
   trained knowledge would be involved with a large state information by
   the control action over a network.  A reward from the global
   environment is reflected to the next optimized control action
   autonomously for network management in distributed networking nodes.
   The Reinforcement Learning(RL) Process have developed and expanded to
   Deep Reinforcement Learning(DRL) with model-driven or data-driven
   technical approaches for learning process.  The trendy technique has
   been widely to attempt and apply to networking fields since Deep
   Reinforcement Learning can be used in practical networking areas
   beyond dynamics and heterogeneous environment disturbances, so that
   in the technique can be intelligently learned in the effective
   strategy.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

Kim, et al.              Expires January 3, 2019                [Page 1]
Internet-Draft            draft-kim-mnrg-rl-03                 July 2018

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Copyright Notice

   Copyright (c) 2018 IETF Trust and the persons identified as the
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   4
   3.  Motivation  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  General Motivation for Reinforcement Learning . . . . . .   4
     3.2.  Reinforcement Learning in networks  . . . . . . . . . . .   4
     3.3.  Deep Reinforcement Learning in networks . . . . . . . . .   4
     3.4.  Motivation in our work  . . . . . . . . . . . . . . . . .   5
   4.  Related Works . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Autonomous Driving System . . . . . . . . . . . . . . . .   5
     4.2.  Network Defect Prediction . . . . . . . . . . . . . . . .   5
     4.3.  Wireless Sensor Network (WSN) . . . . . . . . . . . . . .   6
     4.4.  Routing Enhancement . . . . . . . . . . . . . . . . . . .   6
     4.5.  Routing Optimization  . . . . . . . . . . . . . . . . . .   6
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