Electricity Cost Minimization for Interruptible Workload in Datacenter Servers

摘要

Datacenters have experienced dramatic growth in recent years, and the cost for powering them has become a significant problem. This paper proposes methods to minimize the energy cost for performing a task on a datacenter server before a deadline. We observe that energy prices fluctuate over time, and schedule the task to execute in periods of relatively low cost, despite not having knowledge of future costs during the execution. This problem is studied in several models, starting with an online setting where electricity prices can change arbitrarily. A root phi-competitive algorithm is proposed, where phi is the ratio between the maximum and minimum electricity prices, and this algorithm is also shown to be optimal by proving a matching lower bound. Next, we consider a stochastic setting in which prices vary in a Markovian fashion and propose an optimal algorithm based on dynamic programming. We then study the performance of our algorithms in practice using prices derived from real world data. The results show that the stochastic algorithm is very effective, and achieves cost that is within 3.4 percent of the optimum. Moreover, it performs well compared to several heuristics used in practice.