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Effects of Different Queueing Models on Migration of Virtual Machines

  • Surabhi SachdevaEmail author
  • Neeraj Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Virtualization is an act of creating virtual resources that are made accessible by the application of cloud computing. In case of failure of a virtual machine, it is imperative to shift the processes running on this machine to another. This activity is known as live virtual machine migration and is classified under the major issues of research. Xia (2015) proposed a mathematical model to analyze this problem in which the work has been done in two phases. It is showing a state transition model system in which the M/M/1/K model has been utilized in phase1 to find the rejection probability of the jobs in the phase2. Each virtual machine is considered to have a buffer to store the incoming buffer. The major issue being discussed in this paper is the relation of the rejection probability of jobs with the changing size of the buffer. It actually provides an exhaustive analysis of three different queueing models, i.e., M/M/1/∞, M/M/∞, and M/M/1/K. The simulations are carried out in MATLAB, and the results are analyzed based on the rejection probability of the jobs. It is observed that with an increase in the request arrival rate, the rejection probability of jobs increases. However, with an increase in execution rate, the rejection probability of jobs decreases. If we change the model to M/M/∞, actually, the formulas of request rejection probability and job rejection probability got changed that resulted in a continuous decrease in values of rejection rate lines as compared to the values of the author. Hence, we can say that changing the queueing model is beneficial.

Keywords

Load balancing Queueing models Virtual machine migration Buffer size Rejection probability 

References

  1. 1.
    Xia, Y., Zhou, M., Luo, X., Zhu, Q., Li, J., Huang, Y.: Stochastic modeling and quality evaluation of infrastructure-as-a-service clouds. IEEE Trans. Autom. Sci. Eng. 162–170 (2015)CrossRefGoogle Scholar
  2. 2.
    Sahoo, J., Mohapatra, S., Lath, R.: Virtualization: a survey on concepts, taxonomy and associated security issues. In: 2010 2nd International Conference on Computer and Network Technology (ICCNT), pp. 222–226. IEEE (2010)Google Scholar
  3. 3.
    Strunk, A.: Costs of virtual machine live migration: a survey. In: 2012 IEEE 8th World Congress on Services (SERVICES), pp. 323–329. IEEE (2012)Google Scholar
  4. 4.
    Loganayagi, B., Sujatha, S.: Enhanced cloud security by combining virtualization and policy monitoring techniques. Procedia Eng. 30, 654–661 (2012)CrossRefGoogle Scholar
  5. 5.
    Chen, H.P., Li, S.C.: A queueing based model for performance management on cloud. In: International Conference on Advanced Information Management and Service (IMS), pp. 83–88. IEEE (2011)Google Scholar
  6. 6.
    Anala, M.R., Shetty, J., Shobha, G.: A framework for secure live migration of virtual machines. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 243–248. IEEE (2013)Google Scholar
  7. 7.
    Baghshahi, S.S., Jabbehdari, S., Adabi, S.: Virtual machine migration based on Greedy algorithm in cloud computing. Int. J. Comput. Appl. 96(12) (2014)Google Scholar
  8. 8.
    Cao, J., Andersson, M., Nyberg, C., Kihl, M.: Web server performance modeling using an M/G/1/K*PS queue. In: 10th International Conference on Telecommunications, 2003. ICT 2003, vol. 2, pp. 1501–1506. IEEE (2003)Google Scholar
  9. 9.
    Xiong, K., Perros, H.: Service performance and analysis in cloud computing. In: Services-I, 2009 World Conference, pp. 693–700. IEEE (2009)Google Scholar
  10. 10.
    Dai, Y.S., Yang, B., Dongarra, J., Zhang, G.: Cloud service reliability: modeling and analysis. In: 15th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 1–17. IEEE (2009)Google Scholar
  11. 11.
    Yang, B., Tan, F., Dai, Y. S., Guo, S.: Performance evaluation of cloud service considering fault recovery. In: IEEE International Conference on Cloud Computing, pp. 571–576. Springer, Berlin, Heidelberg (2009)Google Scholar
  12. 12.
    He, S., Guo, L., Ghanem, M., Guo, Y.: Improving resource utilisation in the cloud environment using multivariate probabilistic models. In: 2012 IEEE 5th International Conference Cloud Computing(CLOUD), pp. 574–581. IEEE (2012)Google Scholar
  13. 13.
    Ghosh, R., Trivedi, K.S., Naik, V.K., Kim, D.S.: End-to-end performability analysis for infrastructure-as-a-service cloud: an interacting stochastic models approach. In: 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 125–132. IEEE (2010)Google Scholar
  14. 14.
    Li, B., Li, J., Huai, J., Wo, T., Li, Q., Zhong, L.: Ena cloud: an energy saving application live placement approach for cloud computing environments. IEEE (2009)Google Scholar
  15. 15.
    Karlapudi, H.: Web application performance prediction. In: Proceedings of International Conference on Communication and Computer Networks, IASTED, pp. 281–286 (2004)Google Scholar
  16. 16.
    Mastelic, T., Brandic, I.: Recent trends in energy efficient cloud computing. J. Latex 11(4) (2012)Google Scholar
  17. 17.
    Sarker, T.K., Tang, M.: Performance-driven live migration of multiple virtual machines in datacenters. In: International Conference on Granular Computing (GrC), pp. 253–258. IEEE (2013)Google Scholar
  18. 18.
    Chanchio, K., Thaenkaew, P.: Time-bound, thread-based live migration of virtual machines. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2014, pp. 364–373. IEEE (2014)Google Scholar
  19. 19.
    Zheng, J., Ng, T.E., Sripanidkulchai, K., Liu, Z.: Pacer: a progress management system for live virtual machine migration. IEEE Trans. Cloud Comput. 10(4), 369–382 (2013)Google Scholar
  20. 20.
    Vilaplana, J., Solsona, F., Teixidó, I., Mateo, J., Abella, F., Rius, J.: A queueing theory model for cloud computing. J. Supercomput. 492–507 (2014)CrossRefGoogle Scholar
  21. 21.
    Pham, C., Hong, C.S.: Using queueing model to analyse the live migration process in data centers, pp. 1136–1138. IEEE (2014)Google Scholar
  22. 22.
    Yu, L., Chen, L., Cai, Z., Shen, H., Liang, Y., Pan, Y.: Stochastic load balancing for virtual resource management in datacenters. IEEE Trans. Cloud Comput. IEEE (2014)Google Scholar
  23. 23.
    Kumar, N., Saxena, S.: Migration performance of cloud applications—a quantitative analysis. Procedia Comput. Sci. 45, 823–831 (2015)CrossRefGoogle Scholar
  24. 24.
    Sandhya, S., Revathi, S., NK, C.: Performance analysis and comparative analysis of process migration using genetic algorithm. Int. J. Sci. Eng. Technol. Res. 5(11) (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.K. R. Mangalam UniversityGurugramIndia

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