• DocumentCode
    2733027
  • Title

    Scheduling dynamic load-balancing in parallel and distributed computers using modified genetic algorithm with time dependent fitness function

  • Author

    Mohammadzadeh, Javad ; Moeinzadeh, M-Hossein ; Sharifian-R, Sarah ; Mahdavi, Leila

  • Author_Institution
    Comput. Dept., Islamic Azad Univ. of Karaj Branch, Karaj, Iran
  • Volume
    1
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    894
  • Lastpage
    898
  • Abstract
    Load Balancing has many applications in various systems, but specifically plays a major role in the efficiency of parallel and distributed systems. In these systems, by load balancing we mean scheduling the jobs in a way that every job could be executed concurrently while it is mapped to a processing unit, such as a processor (in a multi-processor system) or a computer (in a grid computer). By developing effective methods the whole program time execution will be decreased and process utilization will be optimized. In this paper, a solution is proposed for dynamic load balancing. Because of the NP-hard nature of the problem, heuristic methods are desired. A simple scheduling method, Round Robin, and Genetic algorithm are discussed as previous methods for this problem and in order to improve the results a new modification of Genetic Algorithm is presented.
  • Keywords
    computational complexity; genetic algorithms; parallel processing; resource allocation; scheduling; NP-hard problem; distributed computers; genetic algorithm; load balancing; parallel computers; process utilization; round robin; scheduling; time dependent fitness function; Application software; Concurrent computing; Distributed computing; Dynamic scheduling; Genetic algorithms; Grid computing; Load management; Optimization methods; Processor scheduling; Round robin;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5358051
  • Filename
    5358051