Adaptive routing point control in virtualized local area networks using particle swarm optimizations

Kensuke Takahashi*, Toshio Hirotsu, Toshiharu Sugawara

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This paper describes methods for controlling routing points of VLAN domains using binary particle swarm optimization (BPSO) and angle modulated particle swarm optimization (AMPSO). Virtual LAN (VLAN) is a technique for virtualizing datalink layer (or L2) and can construct arbitrary logical networks on top of a physical network. However, VLAN often causes much redundant traffic due to inappropriate deployments of network-layer (L3) routing capabilities in VLAN networks. We propose two methods using BPSO and AMPSO, and show that they can adaptively select the routing points dynamically in accordance with the observed traffic patterns and thus reduce the redundant traffic. The convergence features are compared with those of the conventional method on the basis of a statistical method. Then we also show that the scalability of the algorithm using AMPOS is high and thus we can expect that it is applicable to practical large VLAN environments.

Original languageEnglish
Title of host publicationProceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Pages352-360
Number of pages9
DOIs
Publication statusPublished - 2011
Event23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 - Boca Raton, FL, United States
Duration: 2011 Nov 72011 Nov 9

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Conference

Conference23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Country/TerritoryUnited States
CityBoca Raton, FL
Period11/11/711/11/9

Keywords

  • Network management
  • Particle swarm optimization
  • Routing
  • Virtual LAN

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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