TY - GEN
T1 - Energy-efficient task distribution using neural network temperature prediction in a data center
AU - Omori, Minato
AU - Nakajo, Yusuke
AU - Yoda, Minami
AU - Joshi, Yogendra
AU - Nishi, Hiroaki
N1 - Publisher Copyright:
© 2019 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Data center
KW - Load balancing
KW - Neural network
KW - Temperature prediction
KW - Thermal management
UR - http://www.scopus.com/inward/record.url?scp=85079051535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079051535&partnerID=8YFLogxK
U2 - 10.1109/INDIN41052.2019.8972035
DO - 10.1109/INDIN41052.2019.8972035
M3 - Conference contribution
AN - SCOPUS:85079051535
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 1429
EP - 1434
BT - Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Y2 - 22 July 2019 through 25 July 2019
ER -
