ELM-Based Adaptive Live Migration Approach of Virtual Machines

  • Baiyou Qiao
  • Yang Chen
  • Hong Wang
  • Donghai Chen
  • Yanning Hua
  • Han Dong
  • Guoren Wang
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 16)


Due to having many advantages, virtualization technology has been widely used and become a key technique of cloud computing. Live migration of virtual machines is the core and key technique of virtualization fields, but the existing pre-copy live migration approach has the problems of low copy efficiency and long total migration time, so we propose an extreme learning machine (ELM) based adaptive live migration approach of virtual machines (ELMBALMA) in this chapter. Firstly, the approach uses the ELM algorithm to classify the virtual machines according to the type of the running applications, and then choose the best suitable migration algorithms for each type of virtual machines, thereby reduce the time of live migrating of virtual machines. In addition, we proposed a memory compression based live migration algorithm (MCBLMA) for the memory-intensive application scene. The algorithm uses a weight-based measurement method of writable working set, which can accurately measure the writable working set, so that it can reduce the amount of dirty memory page transmission, meanwhile it uses a memory compression algorithm to compress memory pages to be transmitted, and thus reduces the data transmission time. Preliminary experiments show that the proposed approach can significantly reduce the memory pages transmitted, the total migration time and the downtime of virtual machines.


Virtual machine Live migration Memory compression ELM 



This research was supported by the National Natural Science Foundation of China (No. 61073063, 61173029, 61272182 and 61173030), the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China (No. 201105033), and National Digital Ocean Key Laboratory Open Fund Projects (No. KLDO201306).


  1. 1.
  2. 2.
    G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and applications. Neurocomputing 2006(70), 489–501 (2006)CrossRefGoogle Scholar
  3. 3.
    G.B. Huang, L. Chen, Convex incremental extreme learning machine. Neurocomputing 70, 3056–3062 (2007)CrossRefGoogle Scholar
  4. 4.
    F. Cao, B. Liu, D. Sun Park, Image classification based on effective extremelearning machine. Neurocomputing (IJON) 102, 90–97 (2013)CrossRefGoogle Scholar
  5. 5.
    E. Avci, A new method for expert target recognition system: genetic wavelet extreme learning machine (GAWELM). Expert Syst. Appl. (ESWA) 40(10), 3984–3993 (2013)CrossRefGoogle Scholar
  6. 6.
    S.-J. Lin, C. Chang, M.-F. Hsu, Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowl.-Based Syst. (KBS) 39, 214–223 (2013)Google Scholar
  7. 7.
    W. Zheng, Y. Qian, Text categorization based on regularization extreme learning machine. Neural Comput. Appl. (NCA) 22(3–4), 447–456 (2013)CrossRefGoogle Scholar
  8. 8.
    C.P. Sapuntzakis, R. Chandra, B. Pfaff, et al., Optimizing the migration of virtual computers. SIGOPS Oper. Syst. Rev. 36(SI), 377–390 (2002)Google Scholar
  9. 9.
    C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, A. Warfield, Live migration of virtual machines, in Proceedings of the Second Symposium on Networked Systems Design and Implementation (NSDI’05) (2005), pp. 273–286Google Scholar
  10. 10.
    M.R. Hines, K. Gopalan, Post-copy based live virtual machine migration using adaptive pre-paging and dynamic self-ballooning, in Proceedings of the ACM/Usenix International Conference on Virtual Execution, Environments (VEE’09) (2009), pp. 51–60Google Scholar
  11. 11.
    H. Liu, H. Jin, X. Liao, L. Hu, C. Yu, Live migration of virtual machine based on full system trace and replay, in Proceedings of the 18th International Symposium on High Performance, Distributed Computing (HPDC’09) (2009), pp. 101–110Google Scholar
  12. 12.
    H. Jin, L. Deng, S. Wu, X. Shi, X. Pan, Live virtual machine migration with adaptive memory compression, in Proceedings of the 2009 IEEE International Conference on Cluster Computing (Cluster 2009) (2009)Google Scholar
  13. 13.
    F. Ma, F. Liu, Z. Liu, Live virtual machine migration based on improved pre-copy approach, in IEEE International Conference on Software Engineering and Service Sciences (ICSESS) (2010), pp. 230–233Google Scholar
  14. 14.
    Z. Liu, Q. Wenyu, T. Yan, H. Li, K. Li, Hierarchical copy algorithm for Xen live migration, in International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2010), pp. 361–364Google Scholar
  15. 15.
    Z. Liu, W. Qu, W. Liu, K. Li, Xen live migration with slowdown scheduling algorithm, in The 11th International Conference on Parallel and Distributed Computing, Applications and Technologies (2010), pp. 215–221Google Scholar
  16. 16.
    Y. Du, H. Yu, G. Shi, J. Chen, W. Zheng, Microwiper: efficient memory propagation in live migration of virtual machines, in 39th International Conference on Parallel Processing (2010), pp. 142–149Google Scholar
  17. 17.
    X. Wang, A. Chen, H. Feng, Upper integral network with extreme learning mechanism. Neurocomputing 74(16), 2520–2525 (2011)CrossRefGoogle Scholar
  18. 18.
    G.-B. Huang, X. Ding, H. Zhou, Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3), 155–163 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Baiyou Qiao
    • 1
    • 2
  • Yang Chen
    • 2
  • Hong Wang
    • 2
  • Donghai Chen
    • 2
  • Yanning Hua
    • 1
  • Han Dong
    • 1
  • Guoren Wang
    • 2
  1. 1.National Ocean Information CenterTianjinChina
  2. 2.College of Information Science and EngineeringNortheastern UniversityShenyangChina

Personalised recommendations