Data center power management for regulation service using neural network-based power prediction

Ning Liu, Xue Lin, Yanzhi Wang

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

4 Scopus citations

Abstract

The underlying infrastructure of cloud computing relies on data centers monitored and maintained by the cloud service providers. Data centers usually incur enormous power consumption and are expected to have a significant impact on the local power grid due to dramatically increasing power consumption and fluctuation. In order to mitigate such fluctuation and balance the power demand and supply in the power grid in real time, the regulation service (RS) opportunity has been provided, which offers the electricity consumers to dynamically adjust their power consumption and reduce their electricity cost. Data centers can be active RS participants due to their flexibility and controllability in load dispatching and scheduling temporally (within a server) and spatially (among multiple servers). In order for the data centers to provide better RS, prediction on the data center power consumption becomes essential. In this work, we first adopt artificial neural network (ANN)-based method and long short term memory (LSTM) neural network-based method for the prediction of future data center power consumption. Based on the prediction results, we formulate a novel optimal power management problem of data center to minimize the total cost. Experimental results demonstrate that the total cost of the data center can be reduced by up to 20.6% compared with the baseline systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 18th International Symposium on Quality Electronic Design, ISQED 2017
PublisherIEEE Computer Society
Pages367-372
Number of pages6
ISBN (Electronic)9781509054046
DOIs
StatePublished - May 2 2017
Event18th International Symposium on Quality Electronic Design, ISQED 2017 - Santa Clara, United States
Duration: Mar 14 2017Mar 15 2017

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Other

Other18th International Symposium on Quality Electronic Design, ISQED 2017
CountryUnited States
CitySanta Clara
Period3/14/173/15/17

Keywords

  • Data center
  • artificial neural network
  • long short term memory network
  • power management
  • regulation service
  • smart grid

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Liu, N., Lin, X., & Wang, Y. (2017). Data center power management for regulation service using neural network-based power prediction. In Proceedings of the 18th International Symposium on Quality Electronic Design, ISQED 2017 (pp. 367-372). [7918343] (Proceedings - International Symposium on Quality Electronic Design, ISQED). IEEE Computer Society. https://doi.org/10.1109/ISQED.2017.7918343