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
N1 - Publisher Copyright:
© 2016 IEEE.
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.
T2 - 4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016
Y2 - 4 April 2016 through 8 April 2016
ER -