Energy cost optimization for geographically distributed heterogeneous data centers

Abstract

The proliferation of distributed data centers has recently motivated researchers to study energy cost minimization at the geo-distributed level. Researchers have been using models for time-of-use (TOU) electricity pricing and renewable energy sources to help reduce energy costs when performing geographical workload distribution, but have made oversimplifying assumptions at the data center level. Important considerations such as the thermal, power, and co-location interference effects within each data center have a large impact on the performance of workload management techniques. By designing three techniques that possess varying amounts of knowledge of such information, we compare and quantify the benefits of considering detailed models at the data center level, and demonstrate that our best heuristic can on average achieve a cost reduction of 37% compared to state of the art prior work.