Provably-Efficient Job Scheduling for Energy and Fairness in Geographically Distributed Data Centers

Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs, which originate from multiple organizations/users and are scheduled to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm – GreFar – which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints. Gre Far does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost (in terms of an affine combination of energy cost and weighted fairness) arbitrarily close to that of the optimal offline algorithm with future information. Moreover, by appropriately setting the control parameters, Gre Far achieves a desirable tradeoff among energy cost, fairness and latency.