Convex Optimization
Practical Bilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications)
A finite branch-and-bound algorithm for nonconvex quadratic programming via semidefinite relaxations
Mathematical Programming: Series A and B
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
INFOCOM'10 Proceedings of the 29th conference on Information communications
Greening geographical load balancing
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Data center demand response: avoiding the coincident peak via workload shifting and local generation
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
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The operation of a data center consumes a tremendous amount of electricity, and the energy cost accounts for a large portion of the data center's operation cost. This leads to a growing interest towards reducing the energy cost of data centers. One approach advocated in recent studies is to distribute the computation workload among multiple geographically dispersed data centers by exploiting the electricity price differences. However, the impact of load redistributions on the power grid is not well understood yet. This paper takes the first step towards tackling this important issue, by studying how the power grid can take advantage of the data center's 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 power grid operator aims to balance the electric power load in the first stage, and the data centers seek to minimize their total energy cost in the second stage. We show that this two-stage problem is a bilevel program with an indefinite quadratic objective function, which cannot be solved efficiently using standard convex optimization algorithms. Therefore, we reformulate this bilevel optimization problem as a linear program with additional finite complementarity slackness conditions, and propose a branch and bound algorithm to attain the globally optimal solution. The simulation results demonstrate that our proposed scheme can improve the load balancing performance by around 12% in terms of the electric load index and reduce the energy cost of data centers by 46% on average.