Advertisement

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

  • Heyang Xu
  • Yang LiuEmail author
  • Wei Wei
  • Ying Xue
Article
  • 20 Downloads

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.

Keywords

Live migration Migration cost Remaining runtime Virtual machine consolidation 

Notes

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).

References

  1. 1.
    Armbrust, M., Fox, A., Griffith, R.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    Mastelic, T., Oleksiak, A., Claussen, H., et al.: Cloud computing: survey on energy efficiency. ACM Comput. Surv. 47(2), 1–36 (2015)CrossRefGoogle Scholar
  4. 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)Google Scholar
  5. 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. 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)Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Di, S., Kondo, D., Wang, C.: Optimization of composite cloud service processing with virtual machines. IEEE Trans. Comput. 64(6), 1755–1768 (2015)MathSciNetzbMATHGoogle Scholar
  9. 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)Google Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Varasteh, A., Goudarzi, M.: Server consolidation techniques in virtualized data centers: a survey. IEEE Syst. J. 11(2), 772–783 (2017)CrossRefGoogle Scholar
  13. 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)Google Scholar
  14. 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)CrossRefGoogle Scholar
  15. 15.
    Xu, H., Liu, Y., Wei, W., Zhang, W.: Incentive-aware virtual machine scheduling in cloud computing. J. Supercomput. 74(7), 3016–3038 (2018)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 18.
    Xu, H., Yang, B.: Energy-aware resource management in cloud computing considering load balance. J. Inf. Sci. Eng. 33(1), 1–16 (2017)MathSciNetGoogle Scholar
  19. 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. 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. 21.
    Perumal, V., Subbiah, S.: Power-conservative server consolidation based resource management in cloud. Int. J. Netw. Manag. 24(6), 415–432 (2014)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)Google Scholar
  24. 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)Google Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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)Google Scholar
  27. 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)CrossRefGoogle Scholar
  28. 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)Google Scholar
  29. 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)CrossRefGoogle Scholar
  30. 30.
    Mann, Z.Á.: Multicore-aware virtual machine placement in cloud data centers. IEEE Trans. Comput. 65(11), 3357–3369 (2016)MathSciNetCrossRefGoogle Scholar
  31. 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)Google Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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)Google Scholar
  34. 34.
    Google cluster-usage traces (version 2) (2014). http://code.google.com/p/googleclusterdata/. Accessed 16 Oct 2017
  35. 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)Google Scholar
  36. 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)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information Science and EngineeringHenan University of TechnologyZhengzhouChina

Personalised recommendations