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

Misikir Eyob Gebrehiwot, Samuli Aalto, Pasi Lassila

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
Number of pages10
ISBN (Electronic)9781509066100
Publication statusPublished - 10 Jul 2017
MoE publication typeA4 Article in a conference publication
EventIEEE/ACM International Symposium on Cluster, Cloud and Grid Computing - Madrid, Spain
Duration: 14 May 201717 May 2017
Conference number: 17

Publication series

NameIEEE-ACM International Symposium on Cluster Cloud and Grid Computing
ISSN (Print)2376-4414


ConferenceIEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Abbreviated titleCCGRID


  • Energy-Aware task assignment
  • Energy-Response-Time Weighted Sum
  • Markov Decision Processes


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