Proactive Demand Response for Data Centers: A Win-Win Solution

In order to reduce the energy cost of data centers, recent studies suggest distributing computation workload among multiple geographically dispersed data centers by exploiting the electricity price difference. However, the impact of data center load redistribution on the power grid is not well understood yet. This paper takes the first step toward tackling this important issue by studying how the power grid can take advantage of the data centers’ load distribution proactively for the purpose of power load balancing. We model the interactions between power grid and data centers as a two-stage problem where the utility company chooses proper pricing mechanisms to balance the electric power load in the first stage and the data centers seek to minimize their total energy cost by responding to the prices in the second stage. We show that the two-stage problem is a bilevel quadratic program, which is NP-hard and cannot be solved using standard convex optimization techniques. We introduce benchmark problems to derive upper and lower bounds for the solution of the two-stage problem. We further propose a branch and bound algorithm to attain the globally optimal solution, and propose a heuristic algorithm with low computational complexity to obtain an alternative close-to-optimal solution. We also study the impact of background load prediction error using the theoretical framework of robust optimization. The simulation results demonstrate that our proposed scheme can not only improve the power grid reliability, but also reduce the energy cost of data centers.

I. Introduction

Energy management of large and distributed data centers has become an increasingly important problem. With the fast development of cloud computing services, it is now common for a cloud provider (e.g., Google, Microsoft, and Amazon) to build multiple, large, and geographically dispersed data centers across the continent. Each data center may include hundreds of thousands of servers, massive storage equipment, cooling facilities, and power transformers. The energy consumption and cost of data centers hence can be significant [2]. For example, Google reported in 2011 that its data centers continuously draw almost 260 MW of power, which is more than what Salt Lake City consumes [3]. Microsoft’s data center in Washington US consumes 48 MW of power, which is equivalent to the power consumption of about 40,000 households. This has motivated growing research activities toward optimizing the data center operations to reduce the total energy cost. For example, Qureshi et al. in [2] proposed an energy cost minimization method for distributed data centers to exploit electricity price difference. The idea is later extended in [4]–[10].