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Adaptive multi-resource prediction in distributed resource sharing environment

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Adaptive multi-resource prediction in distributed resource sharing environment
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 autocorrelation 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.
Jin Liang, Klara Nahrstedt, Yuanyuan Zhou
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CCGRID
Authors Jin Liang, Klara Nahrstedt, Yuanyuan Zhou
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