Adaptive multi-resource prediction in distributed resource sharing environment

Jin Liang, Klara Nahrstedt, Yuanyuan Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Resource prediction can greatly assist resource selection and scheduling in a distributed resource sharing environment such as a computational grid. Existing resource prediction models are either based on the auto-correlation of a single resource or based on the cross correlation between two resources. In this paper, we propose a multi-resource prediction model (MModel) that uses both kinds of correlations to achieve higher prediction accuracy. We also present two adaptation techniques that enable the MModel to adapt to the time-varying characteristics of the underlying resources. Experimental results with CPU load prediction in both workstation and grid environment show that on average, the adaptive MModel (called MModel-a) can achieve from 6% to more than 90% reduction in prediction errors compared with the autoregressive (AR) model, which has previously been shown to work well for CPU load predictions.

Original languageEnglish (US)
Title of host publication2004 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2004
Pages293-300
Number of pages8
StatePublished - Sep 29 2004
Event2004 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2004 - Chicago, IL, United States
Duration: Apr 19 2004Apr 22 2004

Publication series

Name2004 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2004

Other

Other2004 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2004
CountryUnited States
CityChicago, IL
Period4/19/044/22/04

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Liang, J., Nahrstedt, K., & Zhou, Y. (2004). Adaptive multi-resource prediction in distributed resource sharing environment. In 2004 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2004 (pp. 293-300). (2004 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2004).