• DocumentCode
    2650465
  • Title

    Adaptive Routing Point Control in Virtualized Local Area Networks Using Particle Swarm Optimizations

  • Author

    Takahashi, Kensuke ; Hirotsu, Toshio ; Sugawara, Toshiharu

  • Author_Institution
    Dept. of Comput. Sci. & Eng, Waseda Univ., Tokyo, Japan
  • fYear
    2011
  • fDate
    7-9 Nov. 2011
  • Firstpage
    352
  • Lastpage
    360
  • 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 data link 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.
  • Keywords
    local area networks; particle swarm optimisation; statistical analysis; telecommunication network routing; telecommunication traffic; VLAN domains; adaptive routing point control; angle modulated particle swarm optimization; arbitrary logical network; binary particle swarm optimization; data link layer; physical network; redundant traffic reduction; statistical method; virtualized local area network; Adaptation models; Equations; Local area networks; Modulation; Particle swarm optimization; Redundancy; Routing; Network Management; Particle Swarm Optimization; Routing; Virtual LAN;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
  • Conference_Location
    Boca Raton, FL
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4577-2068-0
  • Electronic_ISBN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2011.59
  • Filename
    6103349