• DocumentCode
    3229776
  • Title

    Optimal multi-installments algorithm for divisible load scheduling

  • Author

    Mingsheng, Shang ; Shixin, Sun

  • Author_Institution
    Sch. of Comput. Sci. & Eng., China Univ. of Electron. Sci. & Technol., Chengdu
  • fYear
    2005
  • fDate
    1-1 July 2005
  • Lastpage
    462
  • Abstract
    In this paper, we study optimal algorithms for scheduling divisible load on a star network in multi-installments, with the objective to minimize the processing time. Firstly, we consider approach based on linear programming. We found that optimal load sizes of each installment are determined by the number of processor and the rate of processor power to the communication speed. If the number of processor is given, we derive the optimal installments. And for achieving optimal processing time it is preferable to use more processors rather than more installments. Next, we consider optimal parameters of periodic multi-installment algorithm. For a given computation system, we derive the analytic expression of optimal installments. We further prove that, for large-scale workloads, any algorithm which keeps the communication link busy is asymptotically optimal, which gives a way to determine how many processors should be involved in processing
  • Keywords
    linear programming; minimisation; processor scheduling; resource allocation; divisible load scheduling; linear programming; optimal multiinstallment algorithm; periodic scheduling; processing time minimization; star network; Computer science; Cost function; Distributed computing; Large-scale systems; Linear programming; Load modeling; Memory management; Processor scheduling; Scheduling algorithm; Sun; divisible loads theory; multi-installment scheduling; optimal installments.; periodic scheduling; processor selecting; start-up costs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    High-Performance Computing in Asia-Pacific Region, 2005. Proceedings. Eighth International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7695-2486-9
  • Type

    conf

  • DOI
    10.1109/HPCASIA.2005.61
  • Filename
    1592305