Dual Optimization of Revenue and Expense in Geo-Distributed Data Centers Using Smart Grid

Exorbitant energy expenses can supersede data center profits. Electricity prices often vary across the geographic regions, caused by gaps in the supply-demand, time of use, and production cost factors. Geo-distributed cloud data centers facilitated by a smart grid and enabled by cloud computing can potentially utilize the spatiotemporal diversity of energy prices to reduce operational expenditure and maximize profit. In this article, we solve the data center profit by formulating it as a constrained multi-objective optimization problem. The proposed solution utilizes an evolutionary algorithm-based higher-level heuristic that optimizes data center revenue and expense objectives simultaneously. The proposed technique provides system managers with trade-off solutions suited to varied operational scenarios. Ours is a multi-step approach, utilizing the optimization scheme to obtain Pareto optimal solutions for the request dispatch and resource allocation problem. When broadly evaluated against a comparative resource optimization scheme, our technique increases revenue while lowering expense and collectively yields a higher profit. It exhibits such performance over a broad range of price changes regardless of the data center's size and utilization level. The extensive simulation results ascertain the effectiveness of the proposed approach across a myriad of system parameters.