Issue No. 06 - June (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.201
Yuanxiong Guo , University of Florida, Gainesville
Yuguang Fang , University of Florida, Gainesville
Electricity expenditure comprises a significant fraction of the total operating cost in data centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this paper, we consider utilizing existing energy storage capabilities in data centers to reduce electricity cost under wholesale electricity markets, where the electricity price exhibits both temporal and spatial variations. A stochastic program is formulated by integrating the center-level load balancing, the server-level configuration, and the battery management while at the same time guaranteeing the quality-of-service experience by end users. We use the Lyapunov optimization technique to design an online algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. We demonstrate the effectiveness of our proposed algorithm through extensive numerical evaluations based on real-world workload and electricity price data sets. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both the spatial and temporal variations in wholesale electricity prices and workload arrival processes.
Electricity, Batteries, Servers, Algorithm design and analysis, Distributed databases, Optimization, wholesale electricity market, Cloud computing, electricity cost, data center, energy storage, Lyapunov optimization
Y. Fang and Y. Guo, "Electricity Cost Saving Strategy in Data Centers by Using Energy Storage," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 1149-1160, 2013.