Journal of Computer Science and Technology


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

Wen-Hong Tian1,2 (田文洪), Senior Member, CCF, Member, ACM, IEEE, Min-Xian Xu3 (徐敏贤), Member, IEEE, CCF, Guang-Yao Zhou1 (周光耀), Kui Wu4 (吴逵), Senior Member, IEEE, Cheng-Zhong Xu5 (须成忠), Fellow, IEEE, and Rajkumar Buyya1,6, Fellow, IEEE   

  1. 1School of Information and Software Engineering, University of Electronic Science and Technology of China,} Chengdu 610054, China
    2Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
    3Institute of Advanced Computing and Digital Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    4Department of Computer Science, University of Victoria, Victoria, BC, V8W 3P6, Canada
    5State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 999078, China
    6School of Computing and Information Systems, University of Melbourne, Melbourne 3010, Australia
  • Contact: Min-Xian Xu
  • About author:Min-Xian Xu is currently an associate professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen. He received the BSc degree in 2012 and the MSc degree in 2015, both in software engineering from University of Electronic Science and Technology of China. He obtained his PhD degree in Computer Science from the University of Melbourne in 2019. His research interests include resource scheduling and optimization in cloud computing. He has co-authored about 40 peer-reviewed papers published in prominent international journals and conferences. His Ph.D. thesis was awarded the 2019 IEEE TCSC Outstanding Ph.D. Dissertation Award.

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, reactive migration is not easy to obtain predefined load balance objectives and 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 algorithms 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 existing online algorithms.


4、结果(Result & Findings):我们分别在真实和合成数据集上进行了算法性能测试,实验结果表明我们的方法相对业界主流算法,在平均利用率、负载不均衡都等指标上,能够有效提升10%-20%的系统性能。

Key words: cloud computing; physical machines; virtual machines; reservation; load balancing; Prepartition;

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