Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with 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

7 Citations (Scopus)

Abstract

To reduce the power consumption in data centers, the coordinated control of the air conditioner and the serversis required. It takes tens of minutes for changes of operationalparameters of air conditioners including outlet air temperatureand volume to be reflected in the temperature distribution inthe whole data center. So, the proactive control of the airconditioners is required according to the prediction temperaturedistribution corresponding to the load on the servers. In thispaper, the temperature distribution and the power efficiencyof air conditioner were predicted by using a machine-learningtechnique, and also we propose a method to follow-up proactivecontrol of the air conditioner under the predicted optimumcondition. Consequently, by the follow-up proactive control ofthe air conditioner and the load of servers, power consumptionreduction of 30% at maximum was demonstrated.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016
Subtitle of host publicationCo-located with the 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-227
Number of pages2
ISBN (Electronic)9781509019618
DOIs
Publication statusPublished - Jun 1 2016
Externally publishedYes
Event4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016 - Berlin, Germany
Duration: Apr 4 2016Apr 8 2016

Publication series

NameProceedings - 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

Conference

Conference4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016
CountryGermany
CityBerlin
Period4/4/164/8/16

Fingerprint

Learning systems
Temperature distribution
Electric power utilization
Air
Servers

Keywords

  • data center
  • machine learning
  • power consumption

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Networks and Communications

Cite this

Tarutani, Y., Hashimoto, K., Hasegawa, G., Nakamura, Y., Tamura, T., Matsuda, K., & Matsuoka, M. (2016). Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning. 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. 226-227). [7484193] (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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC2E.2016.39

Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning. / Tarutani, Yuya; Hashimoto, Kazuyuki; Hasegawa, Go; Nakamura, Yutaka; Tamura, Takumi; Matsuda, Kazuhiro; Matsuoka, Morito.

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. Institute of Electrical and Electronics Engineers Inc., 2016. p. 226-227 7484193 (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).

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

Tarutani, Y, Hashimoto, K, Hasegawa, G, Nakamura, Y, Tamura, T, Matsuda, K & Matsuoka, M 2016, Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning. 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., 7484193, 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, Institute of Electrical and Electronics Engineers Inc., pp. 226-227, 4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016, Berlin, Germany, 4/4/16. https://doi.org/10.1109/IC2E.2016.39
Tarutani Y, Hashimoto K, Hasegawa G, Nakamura Y, Tamura T, Matsuda K et al. Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning. 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. Institute of Electrical and Electronics Engineers Inc. 2016. p. 226-227. 7484193. (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). https://doi.org/10.1109/IC2E.2016.39
Tarutani, Yuya ; Hashimoto, Kazuyuki ; Hasegawa, Go ; Nakamura, Yutaka ; Tamura, Takumi ; Matsuda, Kazuhiro ; Matsuoka, Morito. / Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning. 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. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 226-227 (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).
@inproceedings{0d449c3f28d24a2bbea7e14cc858ea31,
title = "Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning",
abstract = "To reduce the power consumption in data centers, the coordinated control of the air conditioner and the serversis required. It takes tens of minutes for changes of operationalparameters of air conditioners including outlet air temperatureand volume to be reflected in the temperature distribution inthe whole data center. So, the proactive control of the airconditioners is required according to the prediction temperaturedistribution corresponding to the load on the servers. In thispaper, the temperature distribution and the power efficiencyof air conditioner were predicted by using a machine-learningtechnique, and also we propose a method to follow-up proactivecontrol of the air conditioner under the predicted optimumcondition. Consequently, by the follow-up proactive control ofthe air conditioner and the load of servers, power consumptionreduction of 30{\%} at maximum was demonstrated.",
keywords = "data center, machine learning, power consumption",
author = "Yuya Tarutani and Kazuyuki Hashimoto and Go Hasegawa and Yutaka Nakamura and Takumi Tamura and Kazuhiro Matsuda and Morito Matsuoka",
year = "2016",
month = "6",
day = "1",
doi = "10.1109/IC2E.2016.39",
language = "English",
series = "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",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "226--227",
booktitle = "Proceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016",

}

TY - GEN

T1 - Reducing power consumption in data center by predicting temperature distribution and air conditioner efficiency with machine learning

AU - Tarutani, Yuya

AU - Hashimoto, Kazuyuki

AU - Hasegawa, Go

AU - Nakamura, Yutaka

AU - Tamura, Takumi

AU - Matsuda, Kazuhiro

AU - Matsuoka, Morito

PY - 2016/6/1

Y1 - 2016/6/1

N2 - To reduce the power consumption in data centers, the coordinated control of the air conditioner and the serversis required. It takes tens of minutes for changes of operationalparameters of air conditioners including outlet air temperatureand volume to be reflected in the temperature distribution inthe whole data center. So, the proactive control of the airconditioners is required according to the prediction temperaturedistribution corresponding to the load on the servers. In thispaper, the temperature distribution and the power efficiencyof air conditioner were predicted by using a machine-learningtechnique, and also we propose a method to follow-up proactivecontrol of the air conditioner under the predicted optimumcondition. Consequently, by the follow-up proactive control ofthe air conditioner and the load of servers, power consumptionreduction of 30% at maximum was demonstrated.

AB - To reduce the power consumption in data centers, the coordinated control of the air conditioner and the serversis required. It takes tens of minutes for changes of operationalparameters of air conditioners including outlet air temperatureand volume to be reflected in the temperature distribution inthe whole data center. So, the proactive control of the airconditioners is required according to the prediction temperaturedistribution corresponding to the load on the servers. In thispaper, the temperature distribution and the power efficiencyof air conditioner were predicted by using a machine-learningtechnique, and also we propose a method to follow-up proactivecontrol of the air conditioner under the predicted optimumcondition. Consequently, by the follow-up proactive control ofthe air conditioner and the load of servers, power consumptionreduction of 30% at maximum was demonstrated.

KW - data center

KW - machine learning

KW - power consumption

UR - http://www.scopus.com/inward/record.url?scp=84978127421&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978127421&partnerID=8YFLogxK

U2 - 10.1109/IC2E.2016.39

DO - 10.1109/IC2E.2016.39

M3 - Conference contribution

AN - SCOPUS:84978127421

T3 - 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

SP - 226

EP - 227

BT - Proceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -