|Table of Contents|

[1] Huang Gang, Wang Ruchuan, Xie Yongjuan, Shi Xiaojuan, et al. Load prediction of grid computing resourcesbased on ARSVR method [J]. Journal of Southeast University (English Edition), 2009, 25 (4): 451-455. [doi:10.3969/j.issn.1003-7985.2009.04.007]
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Load prediction of grid computing resourcesbased on ARSVR method()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
25
Issue:
2009 4
Page:
451-455
Research Field:
Computer Science and Engineering
Publishing date:
2009-12-30

Info

Title:
Load prediction of grid computing resourcesbased on ARSVR method
Author(s):
Huang Gang Wang Ruchuan Xie Yongjuan Shi Xiaojuan
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Keywords:
grid autoregressive support vector regression algorithm computing resource load prediction
PACS:
TP393
DOI:
10.3969/j.issn.1003-7985.2009.04.007
Abstract:
Based on the monitoring and discovery service 4(MDS4)model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then, a time-series autoregre-ssive prediction model is devised. And an autoregressive support vector regression(ARSVR)monitoring method is put forward to predict the node load of the data grid.Finally, a model for histo-rical observations sequences is set up using the autoregressive(AR)model and the model order is determined.The support vector regression(SVR)model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.

References:

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Memo

Memo:
Biography: Huang Gang(1961—), male, associate professor, huanggang@njupt.edu.cn.
Foundation item: The National High Technology Research and Development Program of China(863 Program)(No.2007AA01Z404).
Citation: Huang Gang, Wang Ruchuan, Xie Yongjuan, et al. Load prediction of grid computing resources based on ARSVR method[J]. Journal of Southeast University(English Edition), 2009, 25(4): 451-455.
Last Update: 2009-12-20