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Open Access

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Peng Cheng Laboratory, Shenzhen 518066, China
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Abstract

As the power demand in data centers is increasing, the power capacity of the power supply system has become an essential resource to be optimized. Although many data centers use power oversubscription to make full use of the power capacity, there are unavoidable power supply risks associated with it. Therefore, how to improve the data center power capacity utilization while ensuring power supply security has become an important issue. To solve this problem, we first define it and propose a risk evaluation metric called Weighted Power Supply Risk (WPSRisk). Then, a method, named Hybrid Genetic Algorithm with Ant Colony System (HGAACS) , is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply system. HGAACS uses historical power data of each server to find a better placement solution by population iteration. HGAACS possesses not only the remarkable local search ability of Ant Colony System (ACS), but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm (GA). To verify the performance of HGAACS, we experimentally compare it with five other placement algorithms. The experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the risk of power supply system.

Keywords

server placementpower utilizationpower supply riskswarm intelligence algorithm

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 158-173
DOI: 10.26599/TST.2023.9010009
Cite this article:
Chen R, Huang H, Luo X, et al. A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers. Tsinghua Science and Technology, 2024, 29(1): 158-173. https://doi.org/10.26599/TST.2023.9010009

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Received: 18 December 2022
Revised: 15 January 2023
Accepted: 16 February 2023
Published: 21 August 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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