Soft Computing

, Volume 21, Issue 5, pp 1301–1314

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

  • Weiwei Lin
  • SiYao Xu
  • Jin Li
  • Lingling Xu
  • Zhiping Peng
Methodologies and Application

DOI: 10.1007/s00500-015-1862-7

Cite this article as:
Lin, W., Xu, S., Li, J. et al. Soft Comput (2017) 21: 1301. doi:10.1007/s00500-015-1862-7

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.

Keywords

Cloud computing Peak characteristics Similarity  Placement of virtual machine Theory proof 

Funding information

Funder NameGrant NumberFunding Note
National Natural Science Foundation of China
  • 61402183
National Natural Science Foundation of China
  • 61272382
National Natural Science Foundation of China
  • 61202466
Guangdong Provincial Science and technology projects
  • 2013B010401024
Guangdong Provincial Science and technology projects
  • 2014A010103022
Guangdong Provincial Science and technology projects
  • 2014A010103008

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Weiwei Lin
    • 1
  • SiYao Xu
    • 1
  • Jin Li
    • 2
  • Lingling Xu
    • 1
  • Zhiping Peng
    • 3
  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Department of Computer ScienceGuangzhou UniversityGuangzhouChina
  3. 3.College of Computer and Electronic InformationGuangdong University of Petrochemical TechnologyMaomingChina

Personalised recommendations