• DocumentCode
    1823470
  • Title

    Execution Time Prediction Using Rough Set Theory in Hybrid Cloud

  • Author

    Fan, Chih-Tien ; Chang, Yue-Shan ; Wang, Wei-Jen ; Yuan, Shyan-Ming

  • Author_Institution
    Dept. of Comp. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • fYear
    2012
  • fDate
    4-7 Sept. 2012
  • Firstpage
    729
  • Lastpage
    734
  • Abstract
    Execution time prediction is an important issue in cloud computing. Predicting the execution time fast and accurately not only can help users to schedule jobs smarter, but also maximize the throughput and minimize the resource consumption of cloud platform. While hybrid cloud provides methods to federate multiple cloud platforms, different cloud platforms have different resource attributes, which will increase the difficulties to predict a job´s execution time. In this paper, we exploit Rough Set Theory (RST), which is a well-known prediction technique that uses the historical data, to predict the execution time of jobs. The evaluation presents that RST can utilize the accuracy of the execution time, while the decision can be made in a short period of time.
  • Keywords
    cloud computing; rough set theory; RST; cloud computing; cloud platform; execution time prediction; hybrid cloud; resource consumption; rough set theory; Approximation methods; Cloud computing; Dynamic scheduling; Educational institutions; Error analysis; Processor scheduling; Set theory; Execution Time Prediction; History Based Approach; Hybrid Cloud; Private Cloud; Public Cloud; Rough Set Theory; Rough Sets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2012 9th International Conference on
  • Conference_Location
    Fukuoka
  • Print_ISBN
    978-1-4673-3084-8
  • Type

    conf

  • DOI
    10.1109/UIC-ATC.2012.41
  • Filename
    6332074