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

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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.

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Wen-Hong Tian, Guang-Yao Zhou & Rajkumar Buyya

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, China

    Wen-Hong Tian

  3. Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Min-Xian Xu

  4. Department of Computer Science, University of Victoria, Victoria, BC, V8W 3P6, Canada

    Kui Wu

  5. State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, 999078, China

    Cheng-Zhong Xu

  6. School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia

    Rajkumar Buyya

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  1. Wen-Hong Tian
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  2. Min-Xian Xu
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  3. Guang-Yao Zhou
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Correspondence to Min-Xian Xu.

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Tian, WH., Xu, MX., Zhou, GY. et al. Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center. J. Comput. Sci. Technol. 38, 773–792 (2023). https://doi.org/10.1007/s11390-022-1214-x

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  • DOI: https://doi.org/10.1007/s11390-022-1214-x

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