Geographical Job Scheduling in Data Centers with Heterogeneous Demands and Servers

The fast proliferation of cloud computing promotes the rapid development of large-scale commercial data centers. Tens or even hundreds of geographically distributed data centers have been deployed for better reliability and quality of services. This brings huge energy consumption for data centers. Previous research has proved that the geographical load balancing technique can achieve significant energy cost savings for geographically distributed data centers. However, existing methods for geographical load balancing often assume data centers with homogeneous servers, and workloads with single-dimension or uniform resource demands. This is an over-simplification in reality, especially when modern data centers are typically constructed from a variety of server classes. In this paper, we systematically study the problem of job scheduling for geographically distributed data centers to embrace the heterogeneity of underlying platforms and workloads. We develop a novel distributed algorithm to solve the problem efficiently based on the alternating direction method of multipliers. Extensive evaluations based on real-life data center topology, traffic traces, and electricity price data show high efficiency and efficacy of our method.