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Cluster Computing

, Volume 21, Issue 1, pp 837–844 | Cite as

Study of cloud service queuing model based on imbedding Markov chain perspective

  • ZheXi YangEmail author
  • Wei Liu
  • Duo Xu
Article
  • 115 Downloads

Abstract

A cloud service model has been built in this article to evaluate the QoS of cloud service system. The Markov chain of the viewpoint has been created by applying imbedding Markov chain approach. The random arrivals of cloud requests based on the non follow-up effectiveness characteristics of the Markov chain have been simulated. A cloud service queuing model has been set up by using queue waiting time, network delay time and server processing time as the measurable indicators of cloud service system serving level, and the effectiveness of the evaluating model of cloud service quality raised in this article has been validated by means of analysis and simulation.

Keywords

Cloud computing Response time Queuing theory Markov chain 

Notes

Acknowledgements

This work was supported by Soft Science Research Program of Zhejiang Province (No.2016C25G2080022), Philosophy and Social Sciences Key Research Base of Hangzhou City-Elect-ronic Commerce and Network Economy Research Center of Hangzhou Normal University (No.2015JD30), and Education Department Scientific Research Project of Zhejiang Province (No.Y201432184).

References

  1. 1.
    Vani, B., Priya, R.C.M.: A survey on the security issues in cloud computing. Int. J. P2P Netw. Trends. Technol. 11, 16–19 (2014)Google Scholar
  2. 2.
    Chard, K., Caton, S., Rana, O., et al.: Social clouds: a retrospective. IEEE Cloud Comput. 2(6), 30–40 (2015)CrossRefGoogle Scholar
  3. 3.
    Du, N.H., Huang, H.L., Li, L.F.: Can online trading survive bad-mouthing? An experimental investigation. Decis. Support Syst. 56(6), 419–426 (2013)CrossRefGoogle Scholar
  4. 4.
    Hakiri, A., Gokhale, A., Berthou, P., et al.: Software-defined networking: challenges and research opportunities for future internet. Comput. Netw. 75, 453–471 (2014)CrossRefGoogle Scholar
  5. 5.
    Montes, Jesús, Sánchez, Alberto, Memishi, Bunjamin, et al.: GMonE: a complete approach to cloud monitoring. Future Gener. Comput. Syst. 29(8), 2026–2040 (2013)CrossRefGoogle Scholar
  6. 6.
    Bastug, E., Bennis, M., Zeydan, E., et al.: Big data meets telcos: a proactive caching perspective. J. Commun. Netw. 17, 549–557 (2015)CrossRefGoogle Scholar
  7. 7.
    Dinh, H.T., Lee, C., Niyato, D.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 18, 1587–1611 (2013)CrossRefGoogle Scholar
  8. 8.
    Lin, C.H., Liu, D., Pang, W., et al.: Sherlock: a semi-automatic framework for quiz generation using a hybrid semantic similarity measure. Cogn. Comput. 7(6), 667–679 (2015)CrossRefGoogle Scholar
  9. 9.
    Li, Q., Yang, Q., He, Q., et al.: Profit-maximizing virtual machine provisioning based on workload prediction in computing cloud. KSII Trans. Internet Inf. Syst. 9, 4850–4966 (2015)Google Scholar
  10. 10.
    Gotelli, N.J., Wener, U.: Statistical challenges in null model analysis. Oikos 121(2), 171–180 (2012)CrossRefGoogle Scholar
  11. 11.
    Ghosh, R., Longo, F.: Scalable analytics for IaaS cloud availability. IEEE Trans. Cloud Comput. 2(1), 57–70 (2014)Google Scholar
  12. 12.
    Bhanu, Kaushik, Honggang, Zhang, Xinyu, Yang, et al.: Providing service assurance in mobile opportunistic networks. Comput. Netw. 74, 114–140 (2014)CrossRefGoogle Scholar
  13. 13.
    Thijs, Baars, Ravi, Khadka, Hristo, Stefanov, et al.: Chargeback for cloud services. Future Gener. Comput. Syst. 41, 91–103 (2014)CrossRefGoogle Scholar
  14. 14.
    Jararweh, Y., Jarrah, M., Kharbutli, M., et al.: CloudExp: a comprehensive cloud computing experimental framework. Simul. Model. Pract. Theory 49, 180–192 (2014)CrossRefGoogle Scholar
  15. 15.
    Su, Q., Chen, L.: A method for discovering clusters of e-commerce interest patterns using click-stream data. Electron. Commer. Res. Appl. 14(1), 1–13 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Pichel, J.C., Rivera, F.F.: Sparse matrix-vector multiplication on the Single-Chip Cloud Computer many-core processor. J. Parallel Distrib. Comput. 73, 1539–1550 (2013)CrossRefzbMATHGoogle Scholar
  17. 17.
    Su, Q., Huang, J.J., Zhao, X.D.: An information propagation model considering incomplete reading behavior in microblog. Phys. A 419(2), 55–63 (2015)CrossRefGoogle Scholar
  18. 18.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resources provisioning algorithms. Software 41(1), 23–50 (2011)Google Scholar
  19. 19.
    Paula, P., Fazendeiro, P., Augusto, C., et al.: Ambiente Colaborativo para Avaliação de Cadeias de Abastecimento Collaborative Environment for Supply Chain Ass-essment [J]. RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação 12, 1–15 (2013)Google Scholar
  20. 20.
    Xu, Z., Mei, L., Liu, Y., Hu, C., Chen, L.: Semantic enhanced cloud environment for surveillance data management using video structural description. Computing 98(1–2), 35–54 (2016)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of StandardizationChina Jiliang UniversityHangzhouChina

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