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Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics

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Abstract

Virtual machine (VM) placement is a fundamental problem about resource scheduling in cloud computing; however, the design and implementation of an efficient VM placement algorithm are very challenging. To better multiplex and share physical hosts in the cloud data centers, this paper presents a VM placement algorithm based on the peak workload characteristics, which models the workload characteristics of VMs with mathematical method, and measures the similarity of VMs’ workload with VM peak similarity. Avoiding virtual machines whose workload has high correlation are placed together, it places the virtual machines with peak workload staggering at different time together, which achieves better VM consolidation through VM peak similarity. This paper focuses on the mathematical analysis of VM peak similarity, and proves that compared to cosine-similarity method and correlation-coefficient method, peak-similarity method is better theoretically. Finally, numerical simulations and algorithm experiments show that our proposed peak-similarity-based placement algorithm outperforms the random placement algorithm and correlation-coefficient-based placement algorithm.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61402183, 61272382 and 61202466), Guangdong Natural Science Foundation (Grant No. S2012030006242), Guangdong Provincial Science and technology projects (Grant Nos. 2013B010401024, 2013B010401005, 2013B090200021, 2014B010117001, 2014A010103022 and 2014A010103008), and Guangzhou Science and Technology Fund of China (Grant Nos. 2012J4300038, LCY201206, 2013J4300061 and 2013Y200077), and Fundamental Research Funds for the Central Universities (Nos. 2014ZM0032 and 2015ZZ0038).

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Authors and Affiliations

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

    Weiwei Lin, SiYao Xu & Lingling Xu

  2. Department of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, China

    Zhiping Peng

Authors
  1. Weiwei Lin
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  2. SiYao Xu
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  3. Jin Li
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  4. Lingling Xu
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  5. Zhiping Peng
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Correspondence to Weiwei Lin.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Lin, W., Xu, S., Li, J. et al. Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput 21, 1301–1314 (2017). https://doi.org/10.1007/s00500-015-1862-7

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  • DOI: https://doi.org/10.1007/s00500-015-1862-7

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