• DocumentCode
    234779
  • Title

    A New Genetic Algorithm for Release-Time Aware Divisible-Load Scheduling

  • Author

    Xiaoli Wang ; Yuping Wang ; Kun Meng

  • Author_Institution
    Sch. of Comput. Sci., Xidian Univ., Xian, China
  • fYear
    2014
  • fDate
    15-16 Nov. 2014
  • Firstpage
    134
  • Lastpage
    138
  • Abstract
    Divisible-load scheduling has become an increasingly important subject in the research of information technologies in recent years. It focuses on finding an efficient scheduling strategy for massive computing in parallel and distributed systems so that the make-span of the workload is minimized. Most existing scheduling models assume that all processors are idle at the beginning of workload assignment. However, in the real parallel and distributed environments, many processors may still in busy when a new workload arrived. Processors may have different waiting time from the busy state to the idle, that is, processors have different release time. This paper proposed a release-time aware divisible-load scheduling model with hybrid time constraints and designed an effective global optimization genetic algorithm to solve it. Finally, the experiment results show the efficiency and effectiveness of the proposed algorithm.
  • Keywords
    genetic algorithms; processor scheduling; distributed systems; global optimization genetic algorithm; hybrid time constraints; information technologies; parallel systems; processor; release-time aware divisible-load scheduling; Computational modeling; Genetic algorithms; Optimal scheduling; Processor scheduling; Program processors; Scheduling; Time factors; divisible-load scheduling; genetic algorithm; hybrid time constraints; release time;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security (CIS), 2014 Tenth International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4799-7433-7
  • Type

    conf

  • DOI
    10.1109/CIS.2014.97
  • Filename
    7016868