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In this paper the authors present a system for online power prediction in virtualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and Virtual Machines (VM) collected dynamically by the system to predict both the physical machine and per VM level power consumption. A real implementation of the system shows that it can achieve average prediction error of less than 10%, outperforming state of the art regression based approaches at negligible runtime overhead.
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