Carbon-Aware Electricity Cost Minimization for Sustainable Data Centers

摘要

In order to simultaneously power and cool hundreds of thousands of servers, large-scale data centers usually consume several to tens of megawatts of electricity. This enormous electricity consumption leads to considerable concerns in the electricity cost including both electricity bills and carbon tax. To achieve a sustainable data center, many Internet service providers begin to build their own on-site renewable energy plants to help reduce the electricity cost. However, considering the performance constraint of delay tolerant workloads and the lack of future information about the time-varying electricity price, carbon emission rate, and available on-site renewable energy, it is a fairly challenging problem that how to schedule the delay tolerant workloads to reduce the electricity cost of a sustainable data center. To address this challenging optimization problem, this paper proposes an online workload scheduling algorithm CECM based on the Lyapunov optimization framework, which is able to tradeoff between the electricity cost and the performance of delay tolerant workloads without any future information about the time-varying system states. With extensive simulations based on the real-life traces, we show that CECM is able to reduce the electricity cost by 9.26 percent, while still guaranteeing the performance constraint of delay tolerant workloads.