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Adaptive multi-resource prediction in distributed resource sharing environment

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3 Author(s)
Jin Liang ; Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA ; Nahrstedt, K. ; Yuanyuan Zhou

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 96% reduction in prediction errors compared with the autoregressive (AR) model, which has previously been shown to work well for CPU load predictions.

Published in:

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

Date of Conference:

19-22 April 2004