Advertisement

An Energy-Efficient Strategy for Virtual Machine Allocation over Cloud Data Centers

  • Xiuchen Qie
  • Shunfu JinEmail author
  • Wuyi Yue
Article
  • 24 Downloads

Abstract

With the increase in the scale of cloud data centers, more attention is being focused on the issue of energy conservation. In order to achieve greener, more efficient computing in cloud data centers, in this paper, we propose an energy-efficient Virtual Machine (VM) allocation strategy with an asynchronous multi-sleep mode and an adaptive task-migration scheme. The VMs hosted in a virtual cluster are divided into two modules, namely, Module I and Module II. The VMs in Module I are always awake, whereas the VMs in Module II will go to sleep independently, if possible. Accordingly, a queuing model with a partial asynchronous multiple vacations is established to capture the working principle of the proposed strategy. Using the method of a matrix-geometric solution, performance measures in terms of the average response time of tasks and the energy saving rate of the system are mathematically derived. Numerical experiments with analysis and simulation are provided to validate the proposed VM allocation strategy and to estimate the influence of system parameters on performance measures. Finally, a system cost function is constructed to trade off different performance measures, and an intelligent searching algorithm is employed to optimize the number of VMs in Module II and the sleeping parameter in the same time.

Keywords

Cloud data center Energy-efficient strategy VM allocation Multi-sleep Task-migration Intelligent searching algorithm 

Notes

Acknowledgements

This work was supported in part by National Science Foundations (Nos. 61872311, 61472342) and Natural Science Foundation of Hebei Province (F2017203141), China, and was supported in part by MEXT, Japan.

References

  1. 1.
    Hintemann, R., Clausen, J.: Green cloud? The current and future development of energy consumption by data centers, networks and end-user devices. In: Proceedings of the 4th International Conference on ICT for Sustainability (ICT4S 2016), pp. 109–115 (2016)Google Scholar
  2. 2.
    Jin, X., Zhang, F., Vasilakos, A., Liu, Z.: Green data centers: A survey, perspectives, and future directions (2016). https://arxiv.org/pdf/1608.00687v1.pdf. Accessed 9 Dec 2017
  3. 3.
    Singh, S., Chana, I.: Resource provisioning and scheduling in clouds: QoS perspective. J. Supercomput. 72(3), 926–960 (2016)CrossRefGoogle Scholar
  4. 4.
    Haddar, I., Raouyane, B., Bellafkih, M.: Generating a service broker framework for service selection and SLA-based provisioning within network environments. In: Proceedings of the 9th International Conference on Ubiquitous and Future Networks (ICUFN 2017), pp. 630–635 (2017)Google Scholar
  5. 5.
    Nakamura, L., Azevedo, L., Batista, B., Meneguette, R., Toledo, C., Estrella, J.: An analysis of optimization algorithms designed to fully comply with SLA in cloud computing. IEEE Latin Am. Trans. 15(8), 1497–1505 (2017)CrossRefGoogle Scholar
  6. 6.
    Hasan, S., Kouki, Y., Ledoux, T., Pazat, J.: Exploiting renewable sources: when green SLA becomes a possible reality in cloud computing. IEEE Trans. Cloud Comput. 5(2), 249–262 (2017)CrossRefGoogle Scholar
  7. 7.
    Arianyan, E., Taheri, H., Khoshdel, V.: Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. J. Netw. Comput. Appl. 78, 43–61 (2017)CrossRefGoogle Scholar
  8. 8.
    Son, J., Dastjerdi, A., Calheiros, R., Buyya, R.: SLA-aware and energy-efficient dynamic overbooking in SDN-based cloud data centers. IEEE Trans. Sustain. Comput. 2(2), 76–89 (2017)CrossRefGoogle Scholar
  9. 9.
    Hosseinimotlagh, S., Khunjush, F., Samadzadeh, R.: SEATS: smart energy-aware task scheduling in real-time cloud computing. J. Supercomput. 71(1), 45–66 (2015)CrossRefGoogle Scholar
  10. 10.
    Luo, J., Zhang, S., Yin, L., Guo, Y.: Dynamic flow scheduling for power optimization of data center networks. In: Proceedings of the 5th International Conference on Advanced Cloud and Big Data (CBD 2017), pp. 57–62 (2017)Google Scholar
  11. 11.
    Duan, L., Zhan, D., Hohnerlein, J.: Optimizing cloud data center energy efficiency via dynamic prediction of CPU idle intervals. In: Proceedings of the 8th IEEE International Conference on Cloud Computing (IEEE CLOUD 2015), pp. 985–988 (2015)Google Scholar
  12. 12.
    Sarji, I., Ghali, C., Chehab, A., Kayssi, A.: CloudESE: Energy efficiency model for cloud computing environments. In: Proceedings of the 2011 International Conference on Energy Aware Computing (ICEAC 2011), pp. 1–6 (2011)Google Scholar
  13. 13.
    Liu, Y., Zhu, H., Lu, K., Wang, X.: Self-adaptive management of the sleep depths of idle nodes in large scale systems to balance between energy consumption and response times. In: Proceedings of the 4th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2012), pp. 633–639 (2012)Google Scholar
  14. 14.
    Jin, S., Hao, S., Yue, W.: Energy-efficient strategy with a speed switch and a multiple-sleep mode in cloud data centers. In: Proceedings of the 12th International Conference on Queueing Theory and Network Applications (QTNA2017), pp. 143–154 (2017)Google Scholar
  15. 15.
    Jin, S., Hao, S., Wang, B.: Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization. J. Commun. 38(12), 10–20 (2017). (in Chinese)Google Scholar
  16. 16.
    Cao, H., Xu, J., Ke, D., Jin, C., Deng, S., Tang, C., Cui, M., Liu, J.: Economic dispatch of micro-grid based on improved particle-swarm optimization algorithm (2016).  https://doi.org/10.1109/NAPS.2016.7747875
  17. 17.
    Zhang, Y., Zhao, Y., Fu, X., Xu, J.: A feature extraction method of the particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization for Brillouin scattering spectra. Opt. Commun. 376, 56–66 (2016)CrossRefGoogle Scholar
  18. 18.
    Tian, D.: Particle swarm optimization with chaos-based initialization for numerical optimization (2016).  https://doi.org/10.1080/10798587.2017.1293881
  19. 19.
    Paxson, V., Floyd, S.: Wide-area traffic: the failure of Poisson modeling. IEEE/ACM Trans. Netw. 3(3), 226–244 (1995)CrossRefGoogle Scholar
  20. 20.
    Tian, N., Zhang, Z.: Vacation Queueing Models: Theory and Applications. Springer, New York (2006)CrossRefzbMATHGoogle Scholar
  21. 21.
    Jiang, M., Hu, J., Zhao, R., Wei, X., Nie, Z.: Hybrid IE-DDM-MLFMA with Gauss–Seidel iterative technique for scattering from conducting body of translation. Appl. Comput. Electromagn. Soc. J. 30(2), 148–156 (2015)Google Scholar
  22. 22.
    Rahmat-Samii, Y., Gies, D., Robinson, J.: Particle swarm optimization (PSO): a novel paradigm for antenna designs. Ursi Radio Sci. Bull. 76(3), 14–22 (2017)Google Scholar
  23. 23.
    Guedria, N.: Improved accelerated PSO algorithm for mechanical engineering optimization problems. Appl. Soft Comput. 40, 455–467 (2016)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.Key Laboratory for Computer Virtual Technology and System Integration of Hebei ProvinceQinhuangdaoChina
  3. 3.Department of Intelligence and InformaticsKonan UniversityKobeJapan

Personalised recommendations