Online Learning-Based Server Provisioning for Electricity Cost Reduction in Data Center

Abstract

This brief conceives a learning system for implementing self-optimization-based dynamic server resource provisioning (DSRP) of data centers under deregulated electricity markets. We formulate the DSRP problem as a constrained Markov decision process to minimize the electricity cost subject to a constraint on the queue delay. Instead of applying conventional Q-learning to solve this problem, a postdecision state learning-based DSRP algorithm having fast convergence is proposed by estimating and exploiting the workload arrival distribution. We further discuss the offline optimization of the DSRP problem, which is used as the performance benchmark of the proposed method. Finally, we evaluate the performance of the proposed scheme by using real workloads and electricity prices.