Efficient OpenFlow based Inbound Load Balancing for Enterprise Networks

Guodong Wang, Peng Liu, Yanxiao Zhao, Jun Li, Min Song

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Inbound load balancing plays an important role in improving the quality of experience for enterprise networks. Traditional traffic engineering approaches fail to achieve fine-grained inbound load balancing because they have limited capability of obtaining detailed network status and lack flexibility for traffic flow scheduling. In this paper, we adopt OpenFlow to achieve effective inbound load balancing. The potential of implementing OpenFlow in an enterprise network is explored to schedule traffic among different inbound links. We also investigate how to avoid frequent scheduling and the feasibility when applying this approach in real networks. In order to evaluate the proposed approach, an emulation platform is developed based on Mininet, POX and Open vSwitch. Emulation is conducted based on an actual network topology. Emulation results verify that the proposed approach is able to achieve effective load balancing for enterprise networks and avoid unnecessary flow scheduling.

Original languageEnglish
Pages (from-to)319-323
Number of pages5
JournalProcedia Computer Science
Volume129
DOIs
StatePublished - 2018
EventInternational Conference on Identification,Information and Knowledgein The Internet of Things, 2017 - Qufu, China
Duration: 19 Oct 201721 Oct 2017

Keywords

  • Balancing
  • Inbound Links
  • OpenFlow
  • SDN
  • Traffic Load

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