Adaptive multi-resource prediction in distributed resource sharing environment

Authors:
Jin Liang;K. Nahrstedt;Yuanyuan Zhou
Affiliations:
Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA;Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA;Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA
Venue:
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Year:
2004

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