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


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.

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@article{Gebrehiwot2017NearOptimalPF, title={Near-Optimal Policies for Energy-Aware Task Assignment in Server Farms}, author={Misikir Eyob Gebrehiwot and Samuli Aalto and Pasi E. Lassila}, journal={2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)}, year={2017}, pages={1017-1026} }