Telecommunication Systems

, Volume 64, Issue 2, pp 245–257 | Cite as

Machine learning based optimized live virtual machine migration over WAN links

  • Moiz Arif
  • Adnan K. Kiani
  • Junaid Qadir


Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %.


Live migration Wide area network Virtual machine Hypervisor 


  1. 1.
    Ahmad, R. W., Gani, A., Hamid, S. H. A., Shiraz, M., Yousafzai, A., & Xia, F. (2015). A survey on virtual machine migration and server consolidation frameworks for cloud data centers. Journal of Network and Computer Applications, 52, 11–25.CrossRefGoogle Scholar
  2. 2.
    Andersen, D., Balakrishnan, H., Kaashoek, F., & Morris, R. (2001). Resilient overlay networks (Vol. 35, No. 5, pp. 131–145). New York: ACM.Google Scholar
  3. 3.
    Andersson, L., & Madsen, T. (2005). Provider Provisioned Virtual Private Network (VPN) Terminology (No. RFC 4026).Google Scholar
  4. 4.
    Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., et al. (2003). Xen and the art of virtualization. ACM SIGOPS Operating Systems Review, 37(5), 164–177.CrossRefGoogle Scholar
  5. 5.
    Beloglazov, A., & Buyya, R. (2010, November). Adaptive threshold based approach for energy-efficient consolidation of virtual machines in cloud data centers. In Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science (p. 4). New York: ACM.Google Scholar
  6. 6.
    Box, G. E., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimentersGoogle Scholar
  7. 7.
    Campbell, A. T., De Meer, H. G., Kounavis, M. E., Miki, K., Vicente, J. B., & Villela, D. (1999). A survey of programmable networks. ACM SIGCOMM Computer Communication Review, 29(2), 7–23.CrossRefGoogle Scholar
  8. 8.
    Chowdhury, N. M. K., & Boutaba, R. (2010). A survey of network virtualization. Computer Networks, 54(5), 862–876.CrossRefGoogle Scholar
  9. 9.
    Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., Pratt, I., & Warfield, A. (2005). Live migration of virtual machines. In Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2 (pp. 273–286). USENIX Association.Google Scholar
  10. 10.
    Cui, L., Li, J., Li, B., Huai, J., Hu, C., Wo, T., Al-Aqrabi, H., & Liu, L. (2013) VMScatter: migrate virtual machines to many hosts. In ACM SIGPLAN Notices (Vol. 48, No. 7, pp. 63–72). New York: ACM.Google Scholar
  11. 11.
    Hemminger, S. (2005, April). Network emulation with NetEm. In Linux conf au (pp. 18–23).Google Scholar
  12. 12.
    Hines, M. R., & Gopalan, K. (2009, March). Post-copy based live virtual machine migration using adaptive pre-paging and dynamic selfballooning. In Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments (pp. 51–60). New York: ACM.Google Scholar
  13. 13.
    Hines, M. R., Deshpande, U., & Gopalan, K. (2009). Post-copy live migration of virtual machines. ACM SIGOPS Operating Systems Review, 43(3), 14–26.CrossRefGoogle Scholar
  14. 14.
    Hirofuchi, T., Nakada, H., Itoh, S., & Sekiguchi, S. (2010, May). Enabling instantaneous relocation of virtual machines with a lightweight vmm extension. In 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), (pp. 73–83). IEEE.Google Scholar
  15. 15.
    Hirofuchi, T., Ogawa, H., Nakada, H., Itoh, S., & Sekiguchi, S. (2009, May). A live storage migration mechanism over wan for relocatable virtual machine services on clouds. In Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (pp. 460–465). IEEE Computer Society.Google Scholar
  16. 16.
  17. 17.
    Kapil, D., Pilli, E. S., & Joshi, R. C. (2013, February). Live virtual machine migration techniques: Survey and research challenges. In Advance Computing Conference (IACC), 2013 IEEE 3rd International (pp. 963–969). IEEE.Google Scholar
  18. 18.
    Kida, T., Takeda, M., Shinohara, A., Miyazaki, M., & Arikawa, S. (1998, March). Multiple pattern matching in LZW compressed text. In Data Compression Conference, 1998. DCC’98. Proceedings (pp. 103–112). IEEE.Google Scholar
  19. 19.
    Liu, H., Jin, H., Liao, X., Yu, C., & Xu, C. Z. (2011). Live virtual machine migration via asynchronous replication and state synchronization. IEEE Transactions on Parallel and Distributed Systems, 22(12), 1986–1999.CrossRefGoogle Scholar
  20. 20.
    Medina, V., & Garca, J. M. (2014). A survey of migration mechanisms of virtual machines. ACM Computing Surveys (CSUR), 46(3), 30.CrossRefGoogle Scholar
  21. 21.
    Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Berlin: Springer.Google Scholar
  22. 22.
    Ramakrishnan, K. K., Shenoy, P., & Van der Merwe, J. (2007, August). Live data center migration across WANs: a robust cooperative context aware approach. In Proceedings of the 2007 SIGCOMM workshop on Internet network management (pp. 262–267). New York: ACM.Google Scholar
  23. 23.
    Quinlan, J. R. (1993). C4. 5: Programs for Machine Learning.Google Scholar
  24. 24.
    Sharma, M. (2010). Compression using Huffman coding. IJCSNS International Journal of Computer Science and Network Security, 10(5), 133–141.Google Scholar
  25. 25.
    Travostino, F., Daspit, P., Gommans, L., Jog, C., De Laat, C., Mambretti, J., et al. (2006). Seamless live migration of virtual machines over the MAN/WAN. Future Generation Computer Systems, 22(8), 901–907.CrossRefGoogle Scholar
  26. 26.
    Wood, T., Ramakrishnan, K., van der Merwe, J., & Shenoy, P. (2010). Cloudnet: A platform for optimized wan migration of virtual machines. University of Massachusetts Technical Report TR-2010-002 .Google Scholar
  27. 27.
    Ziv, J., & Lempel, A. (1977). A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, 23(3), 337–343.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Electrical Engineering & Computer Science (SEECS)National University of Sciences & Technology (NUST)IslamabadPakistan

Personalised recommendations