Energy-efficient task distribution using neural network temperature prediction in a data center

Minato Omori, Yusuke Nakajo, Minami Yoda, Yogendra Joshi, Hiroaki Nishi

研究成果: Conference contribution

抄録

The growing demand for computing resources leads to a serious problem of excessive energy consumption in data centers. In recent studies, energy consumption of both computing and cooling equipment is drawing attention. For improving the energy efficiency of cooling equipment such as computer room air conditioners (CRACs), it is neccesary to predict temperatures in data centers and to optimize thermal management in data centers. In this study, we propose a temperature prediction method for servers in a data center using a neural network. We used the prediction result for distributing task targeting temperature-based load balancing. First, we conducted an experiment in a real data center to evaluate the prediction accuracy of the proposed method. We then simulated task distribution based on the predicted temperatures and compared the maximum CPU temperature with a non-predictive approach. The results indicated that the proposed method can reduce future CPU temperatures successfully compared to the non-predictive approach, though in exchange for high computational cost.

本文言語English
ホスト出版物のタイトルProceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1429-1434
ページ数6
ISBN(電子版)9781728129273
DOI
出版ステータスPublished - 2019 7
イベント17th IEEE International Conference on Industrial Informatics, INDIN 2019 - Helsinki-Espoo, Finland
継続期間: 2019 7 222019 7 25

出版物シリーズ

名前IEEE International Conference on Industrial Informatics (INDIN)
2019-July
ISSN(印刷版)1935-4576

Conference

Conference17th IEEE International Conference on Industrial Informatics, INDIN 2019
国/地域Finland
CityHelsinki-Espoo
Period19/7/2219/7/25

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 情報システム

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