Intelligent Reinforcement-learning-based Network Management
draft-kim-nmrg-rl-05

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Network Management Research Group                               M-S. Kim
Internet-Draft                                                      ETRI
Intended status: Informational                                  Y-H. Han
Expires: January 9, 2020                                       KoreaTech
                                                               Y-G. Hong
                                                                    ETRI
                                                            July 8, 2019

      Intelligent Reinforcement-learning-based Network Management
                          draft-kim-nmrg-rl-05

Abstract

   This document presents intelligent network management based on
   Artificial Intelligent (AI) such as reinforcement-learning
   approaches.  In a heterogeneous network, intelligent management with
   Artificial Intelligent should usually provide real-time connectivity,
   the type of network management with the quality of real-time data,
   and transmission services generated by an application service.  With
   that reason intelligent management system is needed to support real-
   time connection and protection through efficient management of
   interfering network traffic for high-quality network data
   transmission in the both cloud and IoE network systems.
   Reinforcement-learning is one of the machine learning algorithms that
   can intelligently and autonomously provide to management systems over
   a communication network.  Reinforcement-learning has developed and
   expanded with deep learning technique based on model-driven or data-
   driven technical approaches so that these trendy techniques have been
   widely to intelligently attempt an adaptive networking models with
   effective strategies in environmental disturbances over variety of
   networking areas.  For Network AI with the intelligent and effective
   strategies, intent-based network (IBN) can be also considered to
   continuously and automatically evaluate network status under required
   policy for dynamic network optimization.  The key element for the
   intent-based network is that it provides a verification of whether
   the represented network intent is implementable or currently
   implemented in the network.  Additionally, this approach need to
   provide to take action in real time if the desired network state and
   actual state are inconsistent.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   Internet-Drafts are working documents of the Internet Engineering
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Kim, et al.              Expires January 9, 2020                [Page 1]
Internet-Draft            draft-kim-nmrg-rl-05                 July 2019

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Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   4
   3.  Theoretical Approaches  . . . . . . . . . . . . . . . . . . .   4
     3.1.  Reinforcement-learning  . . . . . . . . . . . . . . . . .   4
     3.2.  Deep-reinforcement-learning . . . . . . . . . . . . . . .   4
     3.3.  Advantage Actor Critic (A2C)  . . . . . . . . . . . . . .   5
     3.4.  Asynchronously Advantage Actor Critic (A3C) . . . . . . .   5
     3.5.  Intent-based Network (IBN)  . . . . . . . . . . . . . . .   6
   4.  Reinforcement-learning-based process scenario . . . . . . . .   6
     4.1.  Single-agent with Single-model  . . . . . . . . . . . . .   7
     4.2.  Multi-agents Sharing Single-model . . . . . . . . . . . .   7
     4.3.  Adversarial Self-Play with Single-model . . . . . . . . .   7
     4.4.  Cooperative Multi-agents with Multiple-models . . . . . .   7
     4.5.  Competitive Multi-agents with Multiple-models . . . . . .   8
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