Intelligent Virtual Machine Scheduling Based on CPU Temperature-Involved Server Load Model

Huan Zhou, Jiebei Zhu (), Binbin Chen, Lujie Yu and Heyu Luo
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Huan Zhou: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Jiebei Zhu: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Binbin Chen: Energy Research Institute of China Southern Power Grid Co., Ltd., Guangzhou 510530, China
Lujie Yu: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Heyu Luo: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2025, vol. 18, issue 14, 1-17

Abstract: To reduce the significant energy consumption in data centers, virtual machine scheduling optimization and server consolidation are deployed. However, existing server power load (SPL) models typically adopt linear approximations for model developments, which results in inaccuracy with actual SPL characteristics, hindering the optimal solution of virtual machine scheduling. Therefore, intelligent virtual machine scheduling (IVMS) is proposed based on a CPU temperature-involved server load model for data center energy conservation. The IVMS establishes a novel server power load model considering the influence of CPU temperature to capture the actual server load characteristics. Based on the model, the Q-learning method is utilized to solve the problem with the advantage of global optimization to obtain the scheduling solution that further improves calculation accuracy. The performance of the proposed IVMS is evaluated and compared to existing methods by both simulation and experiments in data centers, proving that the IVMS can better predict SPL characteristics and further reduce server energy consumption.

Keywords: virtual machine scheduling; server power load model; data center; energy conservation; Q-learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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