• DocumentCode
    2250685
  • Title

    Multi-objective Optimization Based Virtual Resource Allocation Strategy for Cloud Computing

  • Author

    Xu, Li ; Zeng, Zhibin ; Ye, Xiucai

  • Author_Institution
    Key Lab. of Network Security & Cryptology, Fujian Normal Univ., Fuzhou, China
  • fYear
    2012
  • fDate
    May 30 2012-June 1 2012
  • Firstpage
    56
  • Lastpage
    61
  • Abstract
    The increasing requirements on cloud computing entail building up large numbers of large-scale data centers which require a surprising amount of energy. With the gradual depletion and price escalation of traditional energy, operating data center in an energy efficient way is an emerging urgent problem. However, most existing researches of resource allocation in datacenter did not take into full consideration how to decrease energy consumption. In this paper, we formulate the energy efficiency virtual resource allocation for cloud computing as a multi-objective optimization problem, which is then solved by intelligent optimization algorithm. The simulation results reveal that the strategy can successfully generate schedule scheme of different numbers of servers-VMs with diverse characteristics in a reasonable time period and decrease the total operating energy of data center effectively.
  • Keywords
    cloud computing; computer centres; energy conservation; optimisation; power aware computing; resource allocation; virtual machines; VM; cloud computing; energy consumption; energy efficiency virtual resource allocation; intelligent optimization algorithm; large-scale data centers; multiobjective optimization problem; Cloud computing; Energy consumption; Energy efficiency; Optimization; Power demand; Resource management; Servers; cloud computing; energy efficiency; green computing; multi-objective optimization; virtual resource allocation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-1536-4
  • Type

    conf

  • DOI
    10.1109/ICIS.2012.74
  • Filename
    6211077