• DocumentCode
    1806260
  • Title

    Study on timely scheduling algorithm for load balance based on Support Vector Machine

  • Author

    Shi Qiaoshuo ; Li Chongchong ; Li Jungang

  • Author_Institution
    School of Computer Science and Software Engineering, Hebei University of Technology, Tianjin, China
  • fYear
    2013
  • fDate
    1-8 Jan. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    A timely scheduling model is studied and a solution on load balance is attempted to explore from the point of machine learning in this paper. An expert system scheduling algorithm based on Support Vector Machine is presented. After research, the corresponding scheduling model is built, which is applied to the load balance of server cluster. Finally, the feasibility and validity of the algorithm is validated through experiments.
  • Keywords
    Accuracy; Nickel; Presses; Random access memory; Servers; Training; Support Vector Machine; load balance; machine learning; scheduling; server cluster;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Conference Anthology, IEEE
  • Conference_Location
    China
  • Type

    conf

  • DOI
    10.1109/ANTHOLOGY.2013.6784996
  • Filename
    6784996
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1806260