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An effective data aggregation based adaptive long term CPU load predictions mechanism on computational grid

Journal Article


Abstract


  • With the development of Internet-based technologies and the rapid growth of scientific computing

    applications, Grid computing becomes more and more attractive. Generally, the execution time of a

    CPU-intensive task on a certain resource is tightly related to the CPU load on this resource. In order to

    estimate the task execution time more accurately to achieve an effective task scheduling, it is significant

    to make an effective long-term load prediction in dynamic Grid environments. Nevertheless, as the

    prediction errors will be gradually accumulated while the best values of prediction parameters may

    vary vigorously, the existing prediction algorithms usually fail to achieve good prediction accuracy in

    the long-term prediction. To address these problems, an effective Data Aggregation based Adaptive Long

    term resource load Point-Prediction mechanism (DA2LPPoint) is proposed in this paper, where a data

    aggregation concept is introduced herein to reduce the number of prediction step. Furthermore, an

    interval based prediction mechanism with probability distribution representation called DA2LPInterval is

    lately proposed to improve the adaptation of prediction results. The experimental results show that the

    DA2LPPoint algorithm can outperform previous prediction methods in regard to mean square error (MSE).

    In addition, the DA2LPInterval algorithm can attain lesser prediction error with stronger representation

    capability; therefore, it is able to provide much more useful information for task scheduling in Grid

    environments.

Authors


  •   Dong, Fang (external author)
  •   Luo, Junzhou (external author)
  •   Song, Aibo (external author)
  •   Cao, Jiuxin (external author)
  •   Shen, Jun

Publication Date


  • 2012

Citation


  • Dong, F., Luo, J., Song, A., Cao, J. & Shen, J. (2012). An effective data aggregation based adaptive long term CPU load predictions mechanism on computational grid. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 28 (7), 1030-1044.

Scopus Eid


  • 2-s2.0-84860290087

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1942

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 1030

End Page


  • 1044

Volume


  • 28

Issue


  • 7

Place Of Publication


  • Netherlands

Abstract


  • With the development of Internet-based technologies and the rapid growth of scientific computing

    applications, Grid computing becomes more and more attractive. Generally, the execution time of a

    CPU-intensive task on a certain resource is tightly related to the CPU load on this resource. In order to

    estimate the task execution time more accurately to achieve an effective task scheduling, it is significant

    to make an effective long-term load prediction in dynamic Grid environments. Nevertheless, as the

    prediction errors will be gradually accumulated while the best values of prediction parameters may

    vary vigorously, the existing prediction algorithms usually fail to achieve good prediction accuracy in

    the long-term prediction. To address these problems, an effective Data Aggregation based Adaptive Long

    term resource load Point-Prediction mechanism (DA2LPPoint) is proposed in this paper, where a data

    aggregation concept is introduced herein to reduce the number of prediction step. Furthermore, an

    interval based prediction mechanism with probability distribution representation called DA2LPInterval is

    lately proposed to improve the adaptation of prediction results. The experimental results show that the

    DA2LPPoint algorithm can outperform previous prediction methods in regard to mean square error (MSE).

    In addition, the DA2LPInterval algorithm can attain lesser prediction error with stronger representation

    capability; therefore, it is able to provide much more useful information for task scheduling in Grid

    environments.

Authors


  •   Dong, Fang (external author)
  •   Luo, Junzhou (external author)
  •   Song, Aibo (external author)
  •   Cao, Jiuxin (external author)
  •   Shen, Jun

Publication Date


  • 2012

Citation


  • Dong, F., Luo, J., Song, A., Cao, J. & Shen, J. (2012). An effective data aggregation based adaptive long term CPU load predictions mechanism on computational grid. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 28 (7), 1030-1044.

Scopus Eid


  • 2-s2.0-84860290087

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1942

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 1030

End Page


  • 1044

Volume


  • 28

Issue


  • 7

Place Of Publication


  • Netherlands