Green Computing with Geo-Distributed Heterogeneous Data Centers

Abstract

Cloud computing in data centers, as an alternative to computing on local machines, has become increasingly popular over the past decade. The need to reduce latency and improve bandwidth for customers has led cloud service providers to scale their data centers across the globe. Such geo-distributed data centers can be physically closer to various groups of target customers, enabling improved performance for their applications. But geo-distributed data centers can be an expensive proposition and require significant investment and justification in terms of return on investment (ROI). In this paper, we present a framework to leverage heterogeneous geo-distributed data centers to reduce electricity costs for cloud computing service providers. Our framework performs intelligent workload management across geo-distributed data centers to minimize the overall energy costs, while considering heterogeneity in data center compute capability, cooling power, workload co-location interference, time-of-use (TOU) electricity pricing, green renewable energy, net metering, and peak demand pricing distribution. Our experimental results indicate that our best technique can achieve an average of 61% cost reduction compared to the state-of-the-art.