A Target Selection Scheme for Learning-Based Switch Migration in Distributed Software-Defined Networks

Xue Hai, Sangheon Pack, Kihun Kim, Hyun Park

Research output: Contribution to journalArticlepeer-review

Abstract

In distributed software-defined networking with multiple controllers, traffic variations can easily cause load imbalance among individual controllers. Thus, switch migration (SM) techniques have been introduced to address this problem. However, appropriate selection of the target controller for SM considering the dynamic nature of networks remains a challenge. In this paper, a learning-based SM (LSM) scheme is proposed to select the most appropriate target controller for SM operation. LSM employs the expectation-maximization algorithm to maximize the likelihood value of the potential target controller by learning the OpenFlow Packet-In message forwarding history. The experimental results demonstrate that LSM substantially outperforms existing schemes in terms of throughput and packet loss rate.

Original languageEnglish
Pages (from-to)13-20
Number of pages8
JournalJournal of Korean Institute of Communications and Information Sciences
Volume47
Issue number1
DOIs
Publication statusPublished - 2022 Jan

Bibliographical note

Publisher Copyright:
© 2022, Korean Institute of Communications and Information Sciences. All rights reserved.

Keywords

  • Controller selection
  • Expectation-maximization algorithm
  • Software-defined networking
  • Switch migration

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Computer Science (miscellaneous)

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