A Fine-Grained Performance-Based Decision Model for Virtualization Application Solution

  • Jianhai Chen
  • Dawei Huang
  • Bei Wang
  • Deshi Ye
  • Qinming He
  • Wenzhi Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7144)


Virtualization technology has been widely applied across a broad range of contemporary datacenters. While constructing a datacenter, architects have to choose a Virtualization Application Solution (VAS) to maximize performance as well as minimize cost. However, the performance of a VAS involves a great number of metric concerns, such as virtualization overhead, isolation, manageability, consolidation, and so on. Further, datacenter architects have their own preference of metrics correlate with datacenters’ specific application scenarios. Nevertheless, previous research on virtualization performance either focus on a single performance concern or test several metrics respectively, rather than gives a holistic evaluation, which leads to the difficulties in VAS decision-making. In this paper, we propose a fine-grained performance-based decision model termed as VirtDM to aid architects to determine the best VAS for them via quantifying the overall performance of VAS according to datacenter architects’ own preference. First, our model defines a measurable, in-depth, fine-grained, human friendly metric system with organized hierarchy to achieve accurate and precise quantitative results. Second, the model harnesses a number of classic Multiple Criteria Decision-Making (MCDM) methods, such as the Analytical Hierarchical Process (AHP), to relieve people’s effort of deciding the weight of different metrics base on their own preference accordingly. Our case study addresses an decision process based on three real VAS candidates as an empirical example exploiting VirtDM and demonstrates the effectiveness of our VirtDM model.


virtualization performance evaluation benchmark datacenter decision making analytic hierarchical process 


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  1. 1.
    Bittman, T., Webinar, G.: Server virtualization: From virtual machines to clouds (2010)Google Scholar
  2. 2.
    Blachman, N., Peek, J.: How google works. (GoogleGuide) (retrieved April 20, 2007)Google Scholar
  3. 3.
    Uhlig, R., Neiger, G., Rodgers, D., Santoni, A., Martins, F., Anderson, A., Bennett, S., Kagi, A., Leung, F., Smith, L.: Intel virtualization technology. Computer 38(5), 48–56 (2005)CrossRefGoogle Scholar
  4. 4.
    Apparao, P., Iyer, R., Zhang, X., Newell, D., Adelmeyer, T.: Characterization & analysis of a server consolidation benchmark. In: Proceedings of the Fourth ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, pp. 21–30. ACM (2008)Google Scholar
  5. 5.
    Padala, P., Zhu, X., Wang, Z., Singhal, S., Shin, K.: Performance evaluation of virtualization technologies for server consolidation. HP Labs Tec. Report (2007)Google Scholar
  6. 6.
    Makhija, V., Herndon, B., Smith, P., Roderick, L., Zamost, E., Anderson, J.: Vmmark: A scalable benchmark for virtualized systems. VMware Inc., CA, Tech. Rep. VMware-TR-2006-002 (September 2006)Google Scholar
  7. 7.
    Matthews, J., Hu, W., Hapuarachchi, M., Deshane, T., Dimatos, D., Hamilton, G., McCabe, M., Owens, J.: Quantifying the performance isolation properties of virtualization systems. In: Proceedings of the 2007 Workshop on Experimental Computer Science, p. 6-es. ACM (2007)Google Scholar
  8. 8.
    McDougall, R., Anderson, J.: Virtualization performance: perspectives and challenges ahead. ACM SIGOPS Operating Systems Review 44(4), 40–56 (2010)CrossRefGoogle Scholar
  9. 9.
    Huber, N., von Quast, M., Brosig, F., Kounev, S.: Analysis of the Performance-Influencing Factors of Virtualization Platforms. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6427, pp. 811–828. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Kundu, S., Rangaswami, R., Dutta, K., Zhao, M.: Application performance modeling in a virtualized environment. In: 2010 IEEE 16th International Symposium on High Performance Computer Architecture, HPCA, pp. 1–10. IEEE (2010)Google Scholar
  11. 11.
    Ye, K., Che, J., Jiang, X., Chen, J., Li, X.: vtestkit: A performance benchmarking framework for virtualization environments. In: The Fifth Annual China Grid Conference, pp. 130–136. IEEE (2010)Google Scholar
  12. 12.
    Moller, K.: Virtual machine benchmarking (2007)Google Scholar
  13. 13.
    Saaty, T.: Decision-making with the ahp: Why is the principal eigenvector necessary. European Journal of Operational Research 145(1), 85–91 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    McVoy, L., Staelin, C.: lmbench: Portable tools for performance analysis. In: Proceedings of the 1996 Annual Conference on USENIX Annual Technical Conference, pp. 23–23. Usenix Association (1996)Google Scholar
  15. 15.
  16. 16.
    Huang, D., Ye, D., He, Q., Chen, J., Ye, K.: Virt-lm: a benchmark for live migration of virtual machine. In: Proceeding of the Second Joint WOSP/SIPEW International Conference on Performance Engineering, pp. 307–316. ACM (2011)Google Scholar
  17. 17.
    Wang, P., Chao, K., Lo, C.: On optimal decision for qos-aware composite service selection. Expert Systems with Applications 37(1), 440–449 (2010)CrossRefGoogle Scholar
  18. 18.
    Tarighi, M., Motamedi, S., Sharifian, S.: A new model for virtual machine migration in virtualized cluster server based on fuzzy decision making. Arxiv preprint arXiv:1002.3329 (2010)Google Scholar
  19. 19.
    Hwang, C., Yoon, K.: Multiple attribute decision making: methods and applications: a state-of-the-art survey, vol. 13. Springer (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianhai Chen
    • 1
  • Dawei Huang
    • 1
  • Bei Wang
    • 1
  • Deshi Ye
    • 1
  • Qinming He
    • 1
  • Wenzhi Chen
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangdogChina

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