• DocumentCode
    3580569
  • Title

    Dynamic Scheduling Strategy for Testing Task in Cloud Computing

  • Author

    Yang Lou ; Tao Zhang ; Jing Yan ; Kun Li ; Yechun Jiang ; Haipeng Wang ; Jing Cheng

  • Author_Institution
    Dept. of Software & Microelectron., Northwestern Polytech. Univ., Xi´an, China
  • fYear
    2014
  • Firstpage
    633
  • Lastpage
    636
  • Abstract
    In the testing Cloud platform, there exist too many testing tasks waiting for scheduling at the same time. How to design scheduling strategy is really a challenging problem. In this paper, we firstly analyze the relationship between the testing tasks and establish the task relationship model. Based on these analyses, we propose a dynamic task scheduling strategy using genetic algorithm, which not only ensures to get the least execution time but also guarantee load balance. The dynamic strategy based on genetic algorithm is being compared with traditional static genetic algorithm on cloudsim. The experimental result shows the high the effectiveness of the proposed strategy.
  • Keywords
    cloud computing; genetic algorithms; scheduling; task analysis; cloud computing; cloud platform; cloudsim; dynamic scheduling strategy; static genetic algorithm; testing task; Biological cells; Dynamic scheduling; Genetic algorithms; Load modeling; Processor scheduling; Testing; cloud computing; dynamic strategy; genetic algorithm; task scheduling; testing task;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Communication Networks (CICN), 2014 International Conference on
  • Print_ISBN
    978-1-4799-6928-9
  • Type

    conf

  • DOI
    10.1109/CICN.2014.141
  • Filename
    7065561