Coordinating Workload Scheduling of Geo-Distributed Data Centers and Electricity Generation of Smart Grid

With the rapidly increasing computing demand, data centers become more and more power-hungry, which incurs substantial electricity cost. Meanwhile, due to the time-dependent demand preference, power grid is suffering high load variations, which results in a large profit loss. In this paper, we consider a cost-efficient workload scheduling with a coordination between a cloud service provider operating multiple geo-distributed data centers and smart grids. The aim is to explore the flexibility of data center power demands to reduce the cost of the cloud service provider and smooth the load variations of smart grids simultaneously. We first present the penalty model of the computation workload scheduling at each data center, and introduce the cost model of smart grids, including power generation cost and the cost due to the power load variations. To jointly minimize the cost of smart grids and penalty of the cloud service provider resulted from workload scheduling, we formulate the objective function as a weighted sum of the cost and the penalty to study the tradeoffs, and obtain the optimal offline solution by the dual decomposition technique. In order to make the coordination implemented in an online fashion, we propose a Receding Horizon Control (RHC) based online algorithm to obtain the suboptimal workload management based on the predicted information, including the future amounts of interactive workload, batch workload, and power load, in the prediction horizon. The simulation results show that with the coordination between the cloud service provider and smart grids, the cost of smart grids can be significantly reduced, by up to 20 percent, and the load variations of smart grids can be well smoothed simultaneously.