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Regular Paper

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

School of Information and Software Engineering, University of Electronic Science and Technology of China Chengdu 610054, China
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Department of Computer Science, University of Victoria, Victoria, BC, V8W 3P6, Canada
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 999078, China
School of Computing and Information Systems, University of Melbourne, Melbourne 3010, Australia
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Abstract

Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines (VMs) after allocation is the traditional way for load balancing and consolidation. However, it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability. Therefore, we provide a new approach, called Prepartition, for load balancing. It partitions a VM request into a few sub-requests sequentially with start time, end time and capacity demands, and treats each sub-request as a regular VM request. In this way, it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal, which supports the resource allocation in a fine-grained manner. Simulations with real-world trace and synthetic data show that our proposed approach with offline version (PrepartitionOff) scheduling has 10%–20% better performance than the existing load balancing baselines under several metrics, including average utilization, imbalance degree, makespan and Capacity_makespan. We also extend Prepartition to online load balancing. Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.

Keywords

load balancingcloud computingphysical machine (PM)virtual machine (VM)reservationPrepartition

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Journal of Computer Science and Technology
Volume 38 Issue 4,
July 2023
Pages 773-792
DOI: 10.1007/s11390-022-1214-x
Cite this article:
Tian W-H, Xu M-X, Zhou G-Y, et al. Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center. Journal of Computer Science and Technology, 2023, 38(4): 773-792. https://doi.org/10.1007/s11390-022-1214-x

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Received: 10 December 2020
Accepted: 26 April 2022
Published: 06 December 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
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