A Server Migration Method Using Q-Learning with Dimension Reduction in Edge Computing

Ryo Urimoto, Yukinobu Fukushima, Yuya Tarutani, Tutomu Murase, Tokumi Yokohira

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Edge computing is a promising computing paradigm that satisfies QoS requirements of delay-sensitive applications. In edge computing, server migration control is indispensable for managing client mobility. As a server migration method for edge computing, the method based on Q-learning has been proposed. However, the method assumes that there is only one application client and the number of destination edge servers is limited to one. In this paper, we propose a server migration method using Q-learning that copes with realistic situations where there are multiple application clients and destination edge servers. The contributions of this paper are as follows: 1) we clarify that, under the situation with multiple application clients and multiple destination edge servers, a straightforward server migration method using Q-learning (RL method) does not scale due to state space explosion, and 2) we propose a server migration method using Q-learning (RL-DR method) that reduces the dimensionality of state space by abstracting the numbers of application clients at all locations into a center of the gravity (COG) of application clients. The simulation results show that 1) RL method shows up to 248% worse performance than conventional server migration methods because of state space explosion and 2) RL-DR method achieves up to 38.3% better performance than the conventional methods.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages301-304
Number of pages4
ISBN (Electronic)9781728191003
DOIs
Publication statusPublished - Jan 13 2021
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: Jan 13 2021Jan 16 2021

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
CountryKorea, Republic of
CityJeju Island
Period1/13/211/16/21

Keywords

  • Q-learning
  • edge computing
  • reinforcement learning
  • server location decision problem
  • server migration

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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