The Study of Using Game Theory for Live Migration Prediction over Cloud Computing

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)


Cloud computing was a technology in recent years which had been concerned. More and more network applications provided client a more convenient experience for use on the cloud computing service. Cloud computing is using virtualization technology. It can not only improve the performance on the server, but including a characteristic dynamic data assignment. Additionally, any server with fault, over loading or maintenance…etc. which need to be stopped, the user is not aware that the service has interrupted, that is because the technology of live migration will quickly backup the remaining data from original server to another server. The study [1] used Gilbert-Elliot model has a capability to predict the probability on dirty page until performing 10 times iteration. From this study, using Game Theory model of reducing predicted number effectively can early determine whether to go the stop-and-copy phase. That saves the time on live migration.


Game Theory Gilbert-Elliot Live Migration Pre-Copy 


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  1. 1.
    Lin, Y.-W.: The Improvement of Predicted Probability with Cloud Computing Memory Live Migration, Department of Electrical Engineering, Tamkang University, Taiwan, R.O.C. (2013)Google Scholar
  2. 2.
    Chen, C.-C.: The effect of Cache-Improving of Live Migration for Virtual Machines. Department of Electrical Engineering, Tamkang University, Taiwan, R.O.C. (2013)Google Scholar
  3. 3.
    Anderson, E., Hobbs, M., Keeton, K., Spence, S., Uysal, M., Veitch, A.: Hippodrome: running circles around storage administration. In: Proceedings of the First Usenix Conference on File and Storage Technologies, FAST (2002)Google Scholar
  4. 4.
    Milojicic, D., Douglis, F., Paindaveine, Y., Wheeler, R., Zhou, S.: Process migration. ACM Computing Surveys 32(3), 241299 (2000)CrossRefGoogle Scholar
  5. 5.
    Huang, J.-S.: Related Dirty Memory Prediction Mechanism for Live Migration in Cloud Computing Systems. Department of Electrical Engineering, Tamkang University (2012)Google Scholar
  6. 6.
    Wang, T.-M.: A Novel P2P Sharing Mechanism based on Social Network and Game Theory., Department of Electrical Engineering, Tamkang University, Taiwan, R.O.C. (2012)Google Scholar
  7. 7.
    Feng, H., Zhang, S., Liu, C., Yan, J., Zhang, M.: P2P Incentive Model On Evolutionary Game Theory. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4 (2008)Google Scholar
  8. 8.
    Ouyang, J.-C., Wang, Y.-B., Hu, X.-H., Lin, Y.-P.: An Incentive Mechanism Using Game Theory for P2P Networks. In: NSWCTC 2009, International Conference on Networks Security, Wireless Communications and Trusted Computing, vol. 2, pp. 715–718 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yen-Liang Chen
    • 1
  • Yao-Chiang Yang
    • 1
  • Wei-Tsong Lee
    • 1
  1. 1.Department of Electrical EngineeringTamkang UniversityTaipeiTaiwan, R.O.C.

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