• DocumentCode
    1797715
  • Title

    A cloud computing resource scheduling scheme based on estimation of distribution algorithm

  • Author

    Niansheng Chen ; Xiaoping Fang ; Xin Wang

  • Author_Institution
    Electron. & Inf. Sch., Shanghai Dianji Univ., Shanghai, China
  • fYear
    2014
  • fDate
    15-17 Nov. 2014
  • Firstpage
    304
  • Lastpage
    308
  • Abstract
    Resource scheduling is one of the key problems of cloud computing, no wonder, the scheduling policy and algorithm affect the performance of the cloud system directly. In order to improve the utilization of cloud computing resources and keep load balancing, a cloud computing resource scheduling algorithm based on estimation of distribution algorithm is proposed. In this algorithm, the idea of population based incremental learning(PBIL) algorithm is fully used. In this paper, cloud computing resource scheduling algorithm model is established firstly, and then objective solution is made by using the PBIL algorithm. Finally, the simulation analysis of algorithm performance is conducted. The simulation results show that the PBIL algorithm can take shorter time to complete task and achieve resource load balancing, especially, for the resource scheduling with large-scale task, the advantages are more apparent.
  • Keywords
    cloud computing; distributed algorithms; learning (artificial intelligence); resource allocation; PHIL algorithm; cloud computing resource scheduling algorithm model; distribution algorithm estimation; population based incremental learning; resource load balancing; Algorithm design and analysis; Cloud computing; Computational modeling; Educational institutions; Processor scheduling; Resource management; Scheduling; PBIL algorithm; cloud computing; estimation of distribution algorithm; resource scheduling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2014 2nd International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-5457-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2014.7009304
  • Filename
    7009304