Towards cost-efficient workload scheduling for a Tango between geo-distributed data center and power grid

Nowadays, data centers consume substantial power, which takes up a considerable portion of local power supply (e.g., smart grid). In this paper, we leverage data center workload scheduling for the coordination between data centers and the smart grid, aiming to reduce the electricity cost of data centers and smooth the load variation of the smart grid simultaneously. We first build cost models of workload scheduling at data centers and the power generation and variation at the smart grid. We formulate the objective function as a weighted sum of the cost of the smart grid and the penalty caused by workload scheduling. Using the dual decomposition method, we then derive the optimal offline solution. To facilitate online implementation, we finally propose a Receding Horizon Control (RHC) based algorithm to obtain the suboptimal solution using limited predicted information. Extensive simulation results show that our proposed scheme can significantly reduce the cost of the smart grid, by up to 20%, while smoothing the load variation simultaneously.