TY - GEN
T1 - Load balancing method using server temperature prediction considering multiple internal heat sources in data centers
AU - Yao, Xin
AU - Omori, Minato
AU - Nishi, Hiroaki
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JST CREST Grant Number JPMJCR19K1. Moreover, the authors express their gratitude to MEXT/JSPS KAKENHI Grant (B) Number JP20H02301.
Funding Information:
This work was supported by JST CREST Grant Number JPMJCR19K1. Moreover, the authors express their gratitude to MEXT/JSPS KAKENHI Grant (B) Number JP20H02301.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/7
Y1 - 2021/3/7
N2 - The increasing demand for cloud computing services to more flexibly use computing resources has led to a severe problem of excessive energy consumption in data centers. In recent research, the energy consumption of computing and cooling equipment has attracted much attention. To reduce the energy consumption in data centers, the energy efficiency of the cooling equipment needs to be considered. This study theoretically modeled the relations between the server's internal heat sources and exhaust temperatures. First, we proposed a model that expands the previous study to describe the temperature change delay and to apply it to multiple internal heat sources. Then, we experimentally evaluated the prediction accuracy of the proposed model using a real server and conducted a simulation to confirm the efficiency of this prediction model for load balancing. The simulation results showed that the proposed method reduced the average maximum server temperature by 0.27 °C compared to the conventional method, which theoretically leads to energy savings of 260 kW per day and 95 MW per year in typical data centers.
AB - The increasing demand for cloud computing services to more flexibly use computing resources has led to a severe problem of excessive energy consumption in data centers. In recent research, the energy consumption of computing and cooling equipment has attracted much attention. To reduce the energy consumption in data centers, the energy efficiency of the cooling equipment needs to be considered. This study theoretically modeled the relations between the server's internal heat sources and exhaust temperatures. First, we proposed a model that expands the previous study to describe the temperature change delay and to apply it to multiple internal heat sources. Then, we experimentally evaluated the prediction accuracy of the proposed model using a real server and conducted a simulation to confirm the efficiency of this prediction model for load balancing. The simulation results showed that the proposed method reduced the average maximum server temperature by 0.27 °C compared to the conventional method, which theoretically leads to energy savings of 260 kW per day and 95 MW per year in typical data centers.
KW - Data center
KW - Load balancing
KW - Server
KW - Thermal management
UR - http://www.scopus.com/inward/record.url?scp=85104133688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104133688&partnerID=8YFLogxK
U2 - 10.1109/ICM46511.2021.9385604
DO - 10.1109/ICM46511.2021.9385604
M3 - Conference contribution
AN - SCOPUS:85104133688
T3 - 2021 IEEE International Conference on Mechatronics, ICM 2021
BT - 2021 IEEE International Conference on Mechatronics, ICM 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Mechatronics, ICM 2021
Y2 - 7 March 2021 through 9 March 2021
ER -
