Skip to main content

Advertisement

SpringerLink
Log in

Migration Cost and Energy-Aware Virtual Machine Consolidation Under Cloud Environments Considering Remaining Runtime

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

By live migration technology, multiple virtual machines (VMs) can be consolidated into a fewer physical servers and the idle ones can be shut down or switched to low-power mode, thus reducing the energy consumption of cloud data centers. However, live migration can result in performance degradation of migrated VMs, or even interrupting their services. At the same time, live migration can also aggravate the overheads of data transmissions and produce additional energy consumption in cloud data centers. All these negative influences belong to migration cost (MC) caused by VM migration, which becomes an important cost factor that can’t be ignored. Otherwise, another important concern, remaining runtime of the migrated VM, also has influence on the efficiency of VM consolidation, which is not well addressed as well. This paper investigates MC-aware VM consolidation problem and formulates the problem as a multi-constraint optimization model by considering migration cost and remaining runtime of VMs. Based on the proposed model, a heuristic algorithm, called MC-aware VM consolidation (MVC) algorithm, is developed. Finally, based on a real-world cloud trace, we conduct extensive experimental studies to verify the validity of the proposed model and algorithm. Experimental results show that, compared with some popular algorithms, MVC algorithm can effectively decrease the migration cost and, at the same time guarantee the energy consumption within a certain low level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Armbrust, M., Fox, A., Griffith, R.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  2. Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  3. Mastelic, T., Oleksiak, A., Claussen, H., et al.: Cloud computing: survey on energy efficiency. ACM Comput. Surv. 47(2), 1–36 (2015)

    Article  Google Scholar 

  4. Cao, J., Wu, Y., Li, M.: Energy efficient allocation of virtual machine in cloud computing environments based on demand forecast. In: Proceedings of the 7th International Conference on Grid and Pervasive Computing, pp. 137–151 (2012)

  5. McCulloch, G.: The true cost of a data center becoming HPC compliant (2017). http://www.datacenterdynamics.com/content-tracks/servers-storage/the-true-cost-of-a-data-center-becoming-hpc-compliant/98890.article. Accessed 16 June 2018

  6. Abada, A., St-Hilaire, M.: Renewable energy curtailment via incentivized inter-datacenter workload migration. In: Proceedings of 11th International Conference on Cloud Computing, pp. 143–157 (2018)

  7. Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)

    Article  Google Scholar 

  8. Di, S., Kondo, D., Wang, C.: Optimization of composite cloud service processing with virtual machines. IEEE Trans. Comput. 64(6), 1755–1768 (2015)

    MathSciNet  MATH  Google Scholar 

  9. Dargie, W.: Estimation of the cost of VM migration. In: Proceedings of the 23rd IEEE International Conference on Computer Communication and Networks, pp. 1–8 (2014)

  10. Ahmad, R.W., Gani, A., Hamid, S.H.A., et al.: A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J. Netw. Comput. Appl. 52, 11–25 (2015)

    Article  Google Scholar 

  11. Ferreto, T., Netto, M., Calheiros, R., De Rose, C.: Server consolidation with migration control for virtualized data centers. Future Gener. Comput. Syst. 27(8), 1027–1034 (2011)

    Article  Google Scholar 

  12. Varasteh, A., Goudarzi, M.: Server consolidation techniques in virtualized data centers: a survey. IEEE Syst. J. 11(2), 772–783 (2017)

    Article  Google Scholar 

  13. Guo, Z., Yao, W., Wang, D.: A virtual machine migration algorithm based on group selection in cloud data center. In: Proceedings of 15th IFIP International Conference on Network and Parallel Computing, pp. 24–36 (2017)

  14. Liu, H., Jin, H., Xu, C.Z., Liao, X.: Performance and energy modeling for live migration of virtual machines. Cluster Comput. 16, 249–264 (2013)

    Article  Google Scholar 

  15. Xu, H., Liu, Y., Wei, W., Zhang, W.: Incentive-aware virtual machine scheduling in cloud computing. J. Supercomput. 74(7), 3016–3038 (2018)

    Article  Google Scholar 

  16. Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)

    Article  Google Scholar 

  17. Gutierrez-Garcia, J.O., Ramirez-Nafarrate, A.: Collaborative agents for distributed load management in cloud data centers using live migration of virtual machines. IEEE Trans. Serv. Comput. 8(6), 916–929 (2015)

    Article  Google Scholar 

  18. Xu, H., Yang, B.: Energy-aware resource management in cloud computing considering load balance. J. Inf. Sci. Eng. 33(1), 1–16 (2017)

    MathSciNet  Google Scholar 

  19. Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y.: Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans. Serv. Comput. (2016). https://doi.org/10.1109/tsc.2016.2616868

    Google Scholar 

  20. Sharma, N., Guddeti, R.M.: Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans. Serv. Comput. (2016). https://doi.org/10.1109/tsc.2016.2596289

    Google Scholar 

  21. Perumal, V., Subbiah, S.: Power-conservative server consolidation based resource management in cloud. Int. J. Netw. Manag. 24(6), 415–432 (2014)

    Article  Google Scholar 

  22. Mastroianni, C., Meo, M., Papuzzo, G.: Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans. Cloud Comput. 1(2), 215–228 (2013)

    Article  Google Scholar 

  23. Marotta, A., Avallone, S.: A simulated annealing based approach for power efficient virtual machines consolidation. In: Proceedings of IEEE 8th International Conference on Cloud Computing, pp. 445–452 (2015)

  24. Cui, L., Cziva, R., Tso, F.P., et al.: Synergistic policy and virtual machine consolidation in cloud data centers. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications, pp. 1–9 (2016)

  25. Zhao, H., Wang, J., Liu, F., et al.: Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Trans. Parallel Distrib. 29(6), 1385–1400 (2018)

    Article  Google Scholar 

  26. Fioccola, G.B., Donadio, P., Canonico, R., et al.: Dynamic routing and virtual machine consolidation in green clouds. In: Proceedings of IEEE International Conference on Cloud Computing Technology and Science, pp. 590–595 (2016)

  27. Ye, K., Wu, Z., Wang, C., et al.: Profiling-based workload consolidation and migration in virtualized data centers. IEEE Trans. Parallel Distrib 26(3), 878–890 (2015)

    Article  Google Scholar 

  28. Wolke, A., Pfeiffer, C.: Improving enterprise VM consolidation with high-dimensional load profiles. In: Proceedings of the 2014 IEEE International Conference on Cloud Engineering, pp. 283–288 (2014)

  29. Tao, F., Li, C., Liao, T., Laili, Y.: BGM-BLA: a new algorithm for dynamic migration of virtual machines in cloud computing. IEEE Trans. Serv. Comput. 9(6), 910–925 (2016)

    Article  Google Scholar 

  30. Mann, Z.Á.: Multicore-aware virtual machine placement in cloud data centers. IEEE Trans. Comput. 65(11), 3357–3369 (2016)

    Article  MathSciNet  Google Scholar 

  31. Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Virtual machine consolidation in cloud data centers using ACO meta-heuristic. In: Proceedings of European Conference on Parallel Processing, pp. 306–317 (2014)

  32. Farahnakian, F., Ashraf, A., Pahikkala, T., et al.: Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans. Serv. Comput. 8(2), 187–198 (2015)

    Article  Google Scholar 

  33. Jammal, M., Hawilo, H., Kanso, A., et al.: Mitigating the risk of cloud services downtime using live migration and high availability-aware placement. In: Proceedings of IEEE International Conference on Cloud Computing Technology and Science, pp. 578–583 (2016)

  34. Google cluster-usage traces (version 2) (2014). http://code.google.com/p/googleclusterdata/. Accessed 16 Oct 2017

  35. Murthy, M.K.M., Sanjay, H.A., Anand, J.: Threshold based auto scaling of virtual machines in cloud environment. In: Proceedings of 11th IFIP International Conference on Network and Parallel Computing, pp. 247–256 (2014)

  36. Calheiros, R., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41, 23–50 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the National Natural and Science Foundation of China (Nos. 61472460, 61702162 and U1504607), Natural Science Project of the Education Department of Henan Province (Nos. 19A520021 and 17A520004), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (No. 17IRTSTHN011), Science and Technology Project of Science and Technology Department of Henan Province (No. 172102110013), Plan for Nature Science Fundamental Research of Henan University of Technology (No. 2018QNJH26), Plan For Scientific Innovation Talent of Henan University of Technology (No. 2018RCJH07) and the Research Foundation for Advanced Talents of Henan University of Technology (2017025).

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, Henan, China

    Heyang Xu, Yang Liu, Wei Wei & Ying Xue

Authors
  1. Heyang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Liu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Liu, Y., Wei, W. et al. Migration Cost and Energy-Aware Virtual Machine Consolidation Under Cloud Environments Considering Remaining Runtime. Int J Parallel Prog 47, 481–501 (2019). https://doi.org/10.1007/s10766-018-00622-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-018-00622-x

Keywords

Search

Navigation