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Journal of Signal Processing Systems

, Volume 91, Issue 10, pp 1077–1089 | Cite as

A Live Migration Algorithm for Containers Based on Resource Locality

  • Weibei Fan
  • Zhijie Han
  • Peng Li
  • Jingya Zhou
  • Jianxi FanEmail author
  • Ruchuan Wang
Article
First Online:

Abstract

With the wide application of cloud computing, the scale of cloud data center network is growing. The virtual machine (VM) live migration technology is becoming more crucial in cloud data centers for the purpose of load balance, and efficient utilization of resources. The lightweight virtualization technique has made virtual machines more portable, efficient and easier to management. Different from virtual machines, containers bring more lightweight, more flexible and more intensive service capabilities to the cloud. Researches on container migration is still in its infancy, especially live migration is still very immature. In this paper, we present the locality live migration model where we take into account the distance, available bandwidth and costs between containers. Furthermore, we conduct comprehensive experiments on a cluster. Extensive simulation results show that the proposed method improves the utilization of resources of servers, and also improves the balance of all kinds of resources on the physical machine.

Keywords

Cloud computing Locality live migration Docker Load balance 
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Notes

Acknowledgements

The subject is sponsored by the by National Key R&D Program of China (2018YFB1003201), National Natural Science Foundation of P. R. China (No.61572337, No.61602333, No.61672296 and No.61702351), the Natural Science Foundation of Jiangsu Province (No.BK20160089), Scientific & Technological Support Project of Jiangsu Province (No.BE2016777, BE2016185), Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Foundation (No.WSNLBKF201701).

References

  1. 1.
    Gai, K., Qiu, M., Xiong, Z., & Liu, M. (2018). Privacy-preserving multi-channel communication in edge-of-things. Future Generation Computer Systems, 85, 190–200.CrossRefGoogle Scholar
  2. 2.
    Guo, C., Wu, H., Tan, K., Shi, L., Zhang, Y., & Lu, S. (2008). DCell: A scalable and fault-tolerant network structure for data centers. Proceedings of International Conference of ACM SIGCOMM Computer Communication Review, 38, 75C86.Google Scholar
  3. 3.
    Guo, C., Lu, G., Li, D., Wu, H., Zhang, X., & Shi, Y. (2009). BCube: A high performance, server centric network architecture for modular data centers. Proceedings of International Conference on ACM SIGCOMM Computer Communication Review, Barcelona, Spain, 39(4), 63–74.CrossRefGoogle Scholar
  4. 4.
    Mysore, R. N., Pamboris, A., Farrington, N. (2009). PortLand: A scalable fault-tolerant layer 2 data center network fabric. Proceedings of ACM SIGCOMM on Data Communication, 39–50.Google Scholar
  5. 5.
    Hamilton, A. J. R., & Jain, N. (2011). VL2: A scalable and flexible data center network. Communications of the ACM, 54(4), 95–104.Google Scholar
  6. 6.
    Li, X., Zhang, R., & Hanzo, L. (2015). Cooperative load balancing in hybrid visible light communications and WiFi. IEEE Transactions on Communications, 63(4), 1319–1329.CrossRefGoogle Scholar
  7. 7.
    Garey, M. R., & Johnson, D. S. (2002). Computers and intractability (Vol. 29). New York: Wh freeman.Google Scholar
  8. 8.
    Docker container, https://github.com/docker/docker, July 16, 2015.
  9. 9.
    Merkel, D. (2014). Docker: Lightweight linux containers for consistent development and deployment. Linux Journal, (239) 2.Google Scholar
  10. 10.
    Houidi, I., Louati, W., & Zeghlache, D. (2008). A distributed and autonomic virtual network mapping framework. Proceedings of International Conference on IEEE Fourth Autonomic and Autonomous Systems (ICAS), 241–247.Google Scholar
  11. 11.
    Shvachko, K., Kuang, H., & Radia, S. (2010). The ha- doop distributed file system. Proceedings of international Conference on IEEE 26th symposium on mass storage systems and technologies (MSST), 1-10.Google Scholar
  12. 12.
    Gai, K., & Qiu, M. (2017). Blend arithmetic operations on tensor-based fully homomorphic encryption over real numbers. IEEE Transactions on Industrial Informatics, 99, 1–1.Google Scholar
  13. 13.
    Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262–273.CrossRefGoogle Scholar
  14. 14.
    Gai, K., Qiu, M., & Zhao, H. (2018). Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing. Journal of Parallel and Distributed Computing, 111, 126–135.CrossRefGoogle Scholar
  15. 15.
    Wang, X., Erickson, A., Fan, J., & Jia, X. (2015). Hailtonian properties of DCell networks. The Computer Journal, 58(11), 2944–2955.CrossRefGoogle Scholar
  16. 16.
    Wang, X., Fan, J., Jia, X., & Lin, C. (2016). An efficient algorithm to construct disjoint path covers of DCell networks. Theoretical Computer Science, 609, 197–210.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Meng, X., Pappas, V., & Zhang, L. (2010). Improving the scalability of data center networks with traffic-aware virtual machine placement. Proceedings of International Conference on IEEE Conference on Computer Communications (INFOCOM), 1–9.Google Scholar
  18. 18.
    Seetharaman, S., Seetharaman, S., & Mahadevan, P. (2010). ElasticTree: Saving energy in data center networks. Proceedings of International Conference on Usenix Conference on Networked Systems Design and Implementation, 1–16.Google Scholar
  19. 19.
    Fan, W., Han, Z., & Wang, R. (2018). An evaluation model and benchmark for parallel computing frameworks. Mobile Information Systems, 3890341(114), 1–14.Google Scholar
  20. 20.
    Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., & Warfield, A. (2005). Live migration of virtual machines. Proceedings of the 2nd Conference on USENIX Association of Symposium on Networked Systems Design & Implementation, 2, 273–286.Google Scholar
  21. 21.
    Sun, M., & Ren, W. (2013). Improvement on dynamic migration technology of virtual machine based on Xen. International Forum on Strategic Technology, 2, 124–127.Google Scholar
  22. 22.
    Ma, F., Liu, F., & Liu, Z. (2010). Live virtual machine migration based on improved pre-copy approach. Proceedings of International Conference on IEEE Software Engineering and Service Sciences, New York, 230–233.Google Scholar
  23. 23.
    Jin, H., Deng, L., Wu, S., Shi, X., & Pan, X. (2014). Live migration of virtual machines by adaptively compressing memory pages. Future Generation Computer Systems, 38, 23–35.CrossRefGoogle Scholar
  24. 24.
    Mohan, A., & Shine, S. (2013). An optimized approach for live VM migration using log records, computing. Proceedings of Fourth International Conference on IEEE Communications and Networking Technologies, New York, 1–4.Google Scholar
  25. 25.
    Hines, M. R., Deshpande, U., & Gopalan, K. (2009). Post-copy live migration of virtual machines. ACM Sigops Operating Systems Review, 43, 14–26.CrossRefGoogle Scholar
  26. 26.
    Deshpande, U., & Keahey, K. (2017). Traffic-sensitive live migration of virtual machines. Future Generation Computer Systems, 72, 118–128.CrossRefGoogle Scholar
  27. 27.
    Li, C., Feng, D., Hua, Y., Xia, W., Qin, L., Huang, Y., & Zhou, Y. (2017). BAC: Bandwidthaware compression for efficient live migration of virtual machines. Proceedings of International Conference on IEEE Conference on Computer Communications (INFOCOM), 1–9.Google Scholar
  28. 28.
    Yu, C., & Huan, F. (2015). Live migration of docker containers through logging and replay. Proceedings of International Conference on Mechatronics and Industrial Informatics, In Advances in Computer Science Research.Google Scholar
  29. 29.
    Indukuri, P. (2016). Performance comparison of Linux containers (LXC) and OpenVZ during live migration: An experiment. Blekinge Institute of Technology.Google Scholar
  30. 30.
    Jaikar, A., Shah, S., & Noh, S. (2016). Performance analysis of NAS and SAN storage for scientific workflow. Proceedings of International Conference on IEEE Platform Technology and Service (PlatCon), 1–4.Google Scholar
  31. 31.
    Wang, H., Li, Y., Zhang, Y., & Jin, D. (2014). Virtual machine migration planning in software-defined networks. IEEE Transactions on Cloud Computing, (99), 1–1.Google Scholar
  32. 32.
    Singh, A., Korupolu, M., & Mohapatra, D. (2008). Server-storage virtualization: Integration and load balancing in data centers. Proceedings of International Conference for IEEE High Performance Computing, Networking, Storage and Analysis, 1–12.Google Scholar
  33. 33.
    Chen, L., Shen, H., & Sapra, K. (2014). RIAL: Resource intensity aware load balancing in clouds. IEEE conference on computer communications (INFOCOM), Toronto, Canada, 1294–1302.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Weibei Fan
    • 1
    • 3
  • Zhijie Han
    • 2
  • Peng Li
    • 3
  • Jingya Zhou
    • 1
  • Jianxi Fan
    • 1
    • 3
    Email author
  • Ruchuan Wang
    • 3
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Institute of Data and Knowledge Engineering Henan UniversityKaifengChina
  3. 3.Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworkNanjingChina

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