Near-Optimal Policies for Energy-Aware Task Assignment in Server Farms

Abstract

Rising energy costs and the push for green computing have inspired a lot of research effort towards energy efficient computing. Incorporating low energy sleep states in server farms is one of the proposed solutions. This paper studies the trade-off between energy and performance that is inherent in such solutions using the popular cost metric Energy-Response-time-Weighted-Sum (ERWS). We apply the Markov Decision Process (MDP) theory to the task assignment problem, and derive a near-optimal dynamic task assignment policy for minimizing the ERWS cost metric. Furthermore, we consider a performance constrained energy minimization problem, and provide an algorithm that builds a dynamic task assignment policy by choosing the right energy weight value for the ERWS cost metric. We also show that the resulting task assignment policy behaves like a modified version of the Join the Shortest Queue (JSQ), having a near-optimal performance by minimizing energy consumption while still obeying response time constraint.