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- W2149201415 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." @default.
- W2149201415 created "2016-06-24" @default.
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- W2149201415 date "2004-11-08" @default.
- W2149201415 modified "2023-09-25" @default.
- W2149201415 title "Adaptive multi-resource prediction in distributed resource sharing environment" @default.
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- W2149201415 doi "https://doi.org/10.1109/ccgrid.2004.1336580" @default.
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