Load balancing method using server temperature prediction considering multiple internal heat sources in data centers

Xin Yao, Minato Omori, Hiroaki Nishi

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

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Mechatronics, ICM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728144429
DOIs
Publication statusPublished - 2021 Mar 7
Event2021 IEEE International Conference on Mechatronics, ICM 2021 - Kashiwa, Japan
Duration: 2021 Mar 72021 Mar 9

Publication series

Name2021 IEEE International Conference on Mechatronics, ICM 2021

Conference

Conference2021 IEEE International Conference on Mechatronics, ICM 2021
CountryJapan
CityKashiwa
Period21/3/721/3/9

Keywords

  • Data center
  • Load balancing
  • Server
  • Thermal management

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

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