• DocumentCode
    2467796
  • Title

    An Efficient Genetic Algorithm for Task Scheduling in Heterogeneous Distributed Computing Systems

  • Author

    Daoud, Mohammad I. ; Kharma, Nawwaf

  • Author_Institution
    Concordia Univ., Montreal
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    3258
  • Lastpage
    3265
  • Abstract
    Task scheduling plays an important role in the operation of distributed computing systems. Because of its importance, several task scheduling algorithms are proposed in the literature, mainly for homogeneous processors. Few scheduling algorithms are proposed for heterogeneous distributed computing systems (HeDCSs). In this paper, we present a new approach which uses a customized genetic algorithm to produce high-quality tasks schedules for HeDCSs. The performance of the new algorithm is compared to that of two leading scheduling algorithms for HeDCSs. The comparison, which is based on both randomly generated task graphs and task graphs of certain real-world numerical applications, exhibits the supremacy of the new algorithm over the older ones, in terms of schedule length, speedup and efficiency.
  • Keywords
    distributed processing; genetic algorithms; graph theory; scheduling; task analysis; genetic algorithm; heterogeneous distributed computing systems; randomly generated task graph; task scheduling; Application software; Computer industry; Distributed computing; Dynamic scheduling; Evolutionary computation; Genetic algorithms; High-speed networks; Job shop scheduling; Processor scheduling; Scheduling algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9487-9
  • Type

    conf

  • DOI
    10.1109/CEC.2006.1688723
  • Filename
    1688723