Toward a Workload Allocation Optimizer for Power Saving in Data Centers

Abstract

The number and scale of data centers are both rapidly increasing due to a continuously growing demand for cloud computing services from many areas. Cloud computing infrastructure relies on a massive amount of HPC servers to process millions of tasks and consumes an enormous amount of power. The implementation of advanced task allocation technology provides a solution for energy efficiency and has therefore become an essential goal for data centers. In this paper, we propose a novel CPU-intensive workload allocation optimizer (WAO) for the task of power saving within data centers. There are three major contributions to this research. First, a data center monitoring module, which continually reports the latest status of the data center and stores operational data. Second, we propose an accurate and efficient server power prediction model for all servers in the HPC clusters. Third, we provide an optimal task assignment engine that evaluates and assigns tasks to the most appropriate server to facilitate minimal power consumption. Our experimental results show that our proposed WAO can obtain about 29.6% power savings and 26% more productivity in a real data center.