This thesis provides a framework for virtual resource placement to optimize energy consumption in data centers. The framework consists of three phases: profiling, task classification, and virtual machine placement, and automatically conducts virtual resource placement for given jobs and tasks. It is shown to achieve a 12% cut of energy consumption in comparison with benchmark methods, implying a save of millions of dollars for an enterprise.
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