Temperature distribution prediction in data centers for decreasing power consumption by machine learning

Yuya Tarutani, Kazuyuki Hashimoto, Go Hasegawa, Yutaka Nakamura, Takumi Tamura, Kazuhiro Matsuda, Morito Matsuoka

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

5 Citations (Scopus)

Abstract

To decrease the power consumption of data centers, coordinated control of air conditioners and task assignment on servers is crucial. It takes tens of minutes for changes of operational parameters of air conditioners including outlet air temperature and volume to be actually reflected in the temperature distribution in the whole data center. Proactive control of the air conditioners is therefore required according to the predicted temperature distribution, which is highly dependent on the task assignment on the servers. In this paper, we apply a machine learning technique for predicting the temperature distribution in a data center. The temperature predictor employs regression models for describing the temperature distribution as it is predicted to be several minutes in the future, with the model parameters trained using operational data monitored at the target data center. We evaluated the performance of the temperature predictor for an experimental data center, in terms of the accuracy of the regression models and the calculation times for training and prediction. The temperature distribution was predicted with an accuracy of 0.095°C. The calculation times for training and prediction were around 1,000 seconds and 10 seconds, respectively. Furthermore, the power consumption of air conditioners was decreased by roughly 30% through proactive control based on the predicting temperature distribution.

Original languageEnglish
Title of host publicationProceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages635-642
Number of pages8
ISBN (Electronic)9781467395601
DOIs
Publication statusPublished - Feb 1 2016
Externally publishedYes
Event7th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2015 - Vancouver, Canada
Duration: Nov 30 2015Dec 3 2015

Publication series

NameProceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015

Conference

Conference7th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2015
CountryCanada
CityVancouver
Period11/30/1512/3/15

Keywords

  • Data center
  • Energy management
  • Machine learning
  • Temperature pridiction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computational Theory and Mathematics

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  • Cite this

    Tarutani, Y., Hashimoto, K., Hasegawa, G., Nakamura, Y., Tamura, T., Matsuda, K., & Matsuoka, M. (2016). Temperature distribution prediction in data centers for decreasing power consumption by machine learning. In Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015 (pp. 635-642). [7396226] (Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CloudCom.2015.49