• DocumentCode
    2977173
  • Title

    An Improved Adaptive Genetic Algorithm in Cloud Computing

  • Author

    Hu Baofang ; Sun Xiuli ; Li Ying ; Sun Hongfeng

  • Author_Institution
    Dept. of Inf. Technol., Shandong Women´s Univ., Jinan, China
  • fYear
    2012
  • fDate
    14-16 Dec. 2012
  • Firstpage
    294
  • Lastpage
    297
  • Abstract
    Aiming at the task scheduling algorithm of cloud environment, an improved adaptive genetic algorithm (PAGA) based on priority mechanism is proposed. This approach for job scheduling not only ensures to make the least execution time but also guarantees the QoS requirement of customer job. An integrated fitness function based on priority is designed to indicate optimized object. This method has advantages of simplifying the iterative operation and reducing iteration times. The proposed algorithm is being compared with the other scheduling algorithms. The experimental result shows that this algorithm has high convergence rate.
  • Keywords
    cloud computing; genetic algorithms; iterative methods; scheduling; PAGA; QoS requirement; cloud computing; improved adaptive genetic algorithm; integrated fitness function; iterative operation; job scheduling; priority mechanism; task scheduling algorithm; Biological cells; Cloud computing; Convergence; Educational institutions; Encoding; Genetic algorithms; Processor scheduling; adaptive genetic algorithm; cloud computing; convergence rate; genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-4879-1
  • Type

    conf

  • DOI
    10.1109/PDCAT.2012.47
  • Filename
    6589280