A reinforcement learning-based power management framework for green computing data centers

Xue Lin, Yanzhi Wang, Massoud Pedram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Various power management techniques have been exploited to reduce the energy consumption of data centers. In this work, we propose a reinforcement learning-based power management framework for data centers, which does not rely on any given stationary assumptions of the job arrival and job service processes. By carefully designing the state space, the action space, and the reward of a learning process, the objective of the reinforcement learning agent coincides with our goal of reducing the server pool energy consumption with reasonable average job response time. Real Google cluster data traces are used to verify the effectiveness of the proposed reinforcement learning-based data center power management framework.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016: Co-located with the 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages135-138
Number of pages4
ISBN (Electronic)9781509019618
DOIs
StatePublished - Jun 1 2016
Event4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016 - Berlin, Germany
Duration: Apr 4 2016Apr 8 2016

Other

Other4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016
CountryGermany
CityBerlin
Period4/4/164/8/16

Keywords

  • power management
  • reinforcement learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Networks and Communications

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  • Cite this

    Lin, X., Wang, Y., & Pedram, M. (2016). A reinforcement learning-based power management framework for green computing data centers. In Proceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016: Co-located with the 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016 (pp. 135-138). [7484175] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC2E.2016.33