• DocumentCode
    2203126
  • Title

    The LGR Method for Task Scheduling in Computational Grid

  • Author

    Navimipour, Nima Jafari ; Khanli, Leili Mohammad

  • Author_Institution
    Young Res. Group, Islamic Azad Univ., Tabriz, Iran
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    1062
  • Lastpage
    1066
  • Abstract
    In the grid systems, scheduling is a major issue in their operation, which is also an important problem in other area such as manufacturing, process control, economics, operation research and, etc. One motivation of grid computing is to aggregate the power of widely distributed resources, and provi.de non-trivial services to users. To achieve this goal, an efficient grid scheduling system is an essential part of the grid.This paper presents and evaluates a new method for task scheduling in grid computing systems to minimize its execution time. With regard to that this problem is a NP-Hard problem; so the evolutionary algorithms are the best choice for solving this problem. In this paper, contrary to prior ways, the new string representation and crossover operator has been used, communication costs hasn´t been ignored and presents as a major factor for reaching to optimum solution.
  • Keywords
    evolutionary computation; grid computing; optimisation; scheduling; task analysis; LGR method; NP-hard problem; computational grid; distributed resources; evolutionary algorithms; grid computing; grid systems; task scheduling; Aggregates; Grid computing; Job shop scheduling; Manufacturing processes; NP-hard problem; Operations research; Power generation economics; Power system economics; Process control; Processor scheduling; DAG; Genetic Algorithm; Grid Computing; Task Scheduling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on
  • Conference_Location
    Phuket
  • Print_ISBN
    978-0-7695-3489-3
  • Type

    conf

  • DOI
    10.1109/ICACTE.2008.24
  • Filename
    4737120