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

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

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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.

server placementpower utilizationpower supply riskswarm intelligence algorithm

Rui Chen、Huikang Huang、Xiaoxuan Luo、Weiwei Lin

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School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China

Peng Cheng Laboratory,Shenzhen 518066,China

National Natural Science Foundation of ChinaGuangdong Major Project of Basic and Applied Basic ResearchGuangzhou Science and Technology Program Key ProjectsGuangdong Marine Economic Development Special Fund ProjectGuangzhou Development Zone Science and Technology ProjectGuangzhou Development Zone Science and Technology Project

620721872019B030302002202007040002GDNRC[2022]172021GH102020GH10

2024

10.26599/TST.2023.9010009

清华大学学报自然科学版(英文版)
清华大学

清华大学学报自然科学版(英文版)

CSTPCDEI
影响因子：0.474
ISSN：1007-0214
年,卷(期)：2024.29(1)
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