• DocumentCode
    2977010
  • Title

    Embedding Virtual Infrastructure Based on Genetic Algorithm

  • Author

    Xiuming Mi ; Xiaolin Chang ; Jiqiang Liu ; Longmei Sun ; Bin Xing

  • Author_Institution
    Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China
  • fYear
    2012
  • fDate
    14-16 Dec. 2012
  • Firstpage
    239
  • Lastpage
    244
  • Abstract
    The virtual network embedding (VNE) problem deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in the offline case. The genetic algorithm (GA) is an excellent approach to solving complex problems in optimization with difficult constraints. This paper explores applying GA to handle the VNE problem. We propose two GA-based VNE algorithms and evaluate them by comparing with the existing state-of-the-art VNE algorithms, including PSO-based VNE approaches. Extensive simulation results validate the capability of the proposed GA-based VNE algorithms in terms of the InP long-term revenue and the VN embedding cost.
  • Keywords
    computational complexity; computer networks; genetic algorithms; telecommunication links; virtualisation; GA-based VNE algorithms; InP long-term revenue; NP-hard problem; PSO-based VNE approaches; VNE problem; genetic algorithm; link constraints; node constraints; physical infrastructure; state-of-the-art VNE algorithms; substrate network; virtual infrastructure embedding; virtual network embedding problem; virtual network requests; Algorithm design and analysis; Bandwidth; Biological cells; Genetic algorithms; Mathematical model; Sociology; Statistics; Evolutionary algorithm; Network virtualization; Optimization; Virtual network embedding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-4879-1
  • Type

    conf

  • DOI
    10.1109/PDCAT.2012.71
  • Filename
    6589270