• DocumentCode
    3384302
  • Title

    Genetic algorithm and ant colony algorithm based Energy-Efficient Task Scheduling

  • Author

    Jianfeng Zhao ; Hongze Qiu

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
  • fYear
    2013
  • fDate
    23-25 March 2013
  • Firstpage
    946
  • Lastpage
    950
  • Abstract
    Task Scheduling is a critical problem in cloud computing platforms. Traditional algorithms mainly focus on shortening the makespan, but seldom mention the energy consumption. This paper proposes a duplication based method to reach multiple targets, including reducing the executing time and energy cost. The main algorithms used are genetic algorithm and ant colony algorithm, with a new dynamic fusion strategy proposed to gain the optimal solution quickly.
  • Keywords
    ant colony optimisation; cloud computing; energy consumption; genetic algorithms; scheduling; ant colony algorithm; cloud computing; duplication based method; dynamic fusion strategy; energy consumption; energy cost; energy-efficient task scheduling; genetic algorithm; Biological cells; Computational modeling; Energy consumption; Genetic algorithms; Heuristic algorithms; Processor scheduling; Sociology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Technology (ICIST), 2013 International Conference on
  • Conference_Location
    Yangzhou
  • Print_ISBN
    978-1-4673-5137-9
  • Type

    conf

  • DOI
    10.1109/ICIST.2013.6747695
  • Filename
    6747695