Soft Computing

, Volume 21, Issue 5, pp 1301–1314 | Cite as

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

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 

References

  1. Agrawal S, Bose SK, Sundarrajan S (2009) Grouping genetic algorithm for solving the server consolidation problem with conflicts. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation. ACM, pp 1–8Google Scholar
  2. Calheiros NR, Rajiv R et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50Google Scholar
  3. Chen R, Qi D, Lin W, Li J (2014) An integrated scheduling algorithm for virtual machine system on asymmetric multi-core processors. Chin J Comput 37(7):1466–1477Google Scholar
  4. Chen M, Zhang H, Su YY et al (2011) Effective VM sizing in virtualized data centers. In: Proceedings of integrated network management (IM), 2011 IFIP/IEEE international symposium on. IEEE, pp 594–601Google Scholar
  5. Chen K, Zheng W (2009) Cloud computing: system instances and current research. J Softw 20(5):1337–1348CrossRefGoogle Scholar
  6. Dong J, Wang H, Li Y, Cheng S (2014) Improving energy efficiency and network performance in IaaS cloud with virtual machine placement. J Commun 35(1):72–81Google Scholar
  7. Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared, GCE ’08 grid computing environments workshop, pp 1–10Google Scholar
  8. Gao Y, Guan H, Qi Z et al (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242MathSciNetCrossRefMATHGoogle Scholar
  9. Hirofuchi T, Nakada H, Ogawa H et al (2010) Eliminating datacenter idle power with dynamic and intelligent vm relocation. Distributed computing and artificial intelligence. Springer, Berlin, pp 645–648Google Scholar
  10. Hu J, Gu J, Sun G (2010) A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In: Parallel architectures, algorithms and programming (PAAP), 2010 third international symposiumon. IEEE, pp 89–96Google Scholar
  11. Kim J, Ruggiero M, Atienza D, et al (2013) Correlation-aware virtual machine allocation for energy-efficient datacenters. In: Proceedings of the conference on design, automation and test in Europe. EDA consortium, pp 1345–1350Google Scholar
  12. Li M, Bi J, Li Z (2014) Resource-scheduling-waiting-aware virtual machine consolidation. J Softw 25(7):1388–1402Google Scholar
  13. Lin W, Wang JZ, Liang C, Qi D (2011) A threshold-based dynamic resource allocation scheme for cloud computing. Proc Eng 23:695–703CrossRefGoogle Scholar
  14. Lin W, Liu B, Zhu L, Qi D (2013) CSP-based resource allocation model and algorithms for energy-efficient cloud computing. J Commun 12:33–41Google Scholar
  15. Lin W, Zhu C, Li J et al (2015) Novel algorithms and equivalence optimisation for resource allocation in cloud computing. Int J Web Grid Serv 11(2):193–210CrossRefGoogle Scholar
  16. Lin W, Qi D (2012) Survey of resource scheduling in cloud computing. Comput Sci 39(10):1–6Google Scholar
  17. Liu Z, Wang S, Sun Q, Yang F (2012) Energy-aware intelligent optimization algorithm for virtual machine replacement. J Huazhong Univ Sci Technol (Nat Sci Edn) 40(S1):398–402Google Scholar
  18. Meng X, Isci C, Kephart J et al (2010) Efficient resource provisioning in compute clouds via vm multiplexing. In: Proceedings of the 7th international conference on autonomic computing. ACM, pp 11–20Google Scholar
  19. Nakada H, Hirofuchi T (2009) Toward virtual machine packing optimization based on genetic algorithm.LNCS 5518:Berlin, Heidelberg: proceedings of the 10th international work conference on artificial neural networks: part 2: distributed computing, artificial intelligence bioinformatics soft computing and ambient assisted living, pp 651–654Google Scholar
  20. Wang X, Wang Y, Cui Y (2014) An energy-aware bi-level optimization model for multi-job scheduling problems under cloud computing. Soft Comput. doi:10.1007/s00500-014-1506-3
  21. Wei L, Huang T, Chen J, Liu Y (2013) Workload prediction-based algorithm for consolidation of virtual machines. J Electron Inf Technol 35(6):1271–1276CrossRefGoogle Scholar
  22. Xu B, Peng Z, Xiao F et al (2014) Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19(8):2265–2273CrossRefGoogle Scholar
  23. Zamanifar K, Nasri N, Nadimi-Shahraki M (2012) Data-aware virtual machine placement and rate allocation in cloud environment. In: Proceedings of 2012 second international conference on advanced computing and communication technologies (ACCT). IEEE, pp 357–360Google Scholar

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