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An efficient energy-aware and service quality improvement strategy applied in cloud computing

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Abstract

Reducing the energy consumption while guaranteeing the quality of service (QoS) in the cloud data centers is challenge task for cloud providers. Dynamic virtual machine (VM) consolidation technology is regarded as a promising approach to satisfy goals. Considering dynamic workload of physical machine (PM) results in VM migration and high resources utilization of PM results in resources contention among VMs that affects working performance of VMs. Hence, it is vital to provide an efficient approach for dynamic VM placement during the consolidation to achieve the objectives while alleviating resources contention among VMs in the data centers. In this paper, the proposed strategy called LBVMP aims to build a novel conception consisting of a balancing flat surface of a PM in terms of CPU, RAM, bandwidth (BW) and another proportion flat surface that the remaining resources capacity of the targeted PM was divided by the request resources (CPU, RAM and BW) of a VM. Then LBVMP calculates the distance between two plats to evaluate VM allocation solutions. Extensive experimental results based on the CloudSim simulator demonstrate that compared with the state-of-the-art algorithm BCAVMP, the proposed strategy enables to reduce the cloud data centers of energy consumption, the number of migrations, SLAV, ESV by an average of 3.50%, 9.40%, 78.40%, 79.91%, respectively.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Hsieh, S.Y., Liu, C.S., Buyya, R., Zomaya, A.Y.: Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers[J]. J. Parallel Distrib. Comput. 139, 99–109 (2020). https://doi.org/10.1016/j.jpdc.2019.12.014

    Article  Google Scholar 

  2. Azizi, S., Zandsalimi, M., Li, D.: An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Clust. Comput. 23(4), 3421–3434 (2020)

    Article  Google Scholar 

  3. Baalamurugan, K., Vijay Bhanu, S.: A multi-objective krill herd algorithm for virtual machine placement in cloud computing. J. Supercomput. 76(6), 4525–4542 (2020)

    Article  Google Scholar 

  4. Barthwal, V., Rauthan, M.M.S.: AntPu: a meta-heuristic approach for energy-efficient and SLA aware management of virtual machines in cloud computing. Memet. Comput. 13(1), 91–110 (2021)

    Article  Google Scholar 

  5. 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 

  6. 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 

  7. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  8. Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Multi-objective, decentralized dynamic virtual machine consolidation using ACO metaheuristic in computing clouds. arXiv preprint (2017). arXiv:1706.06646

  9. Fu, X., Zhou, C.: Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Front. Comput. Sci. 9(2), 322–330 (2015)

    Article  MathSciNet  Google Scholar 

  10. Fu, X., Zhou, C.: Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Front. Comput. Sci. 9(2), 322–330 (2015)

    Article  MathSciNet  Google Scholar 

  11. Garg, N., Singh, D., Goraya, M.S.: Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method. J. Supercomput. 78(4), 6006–6034 (2022)

    Article  Google Scholar 

  12. Gharehpasha, S., Masdari, M., Jafarian, A.: Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm. Clust. Comput. 24(2), 1293–1315 (2021)

    Article  Google Scholar 

  13. Ghobaei-Arani, M., Rahmanian, A.A., Shamsi, M., Rasouli-Kenari, A.: A learning-based approach for virtual machine placement in cloud data centers. Int. J. Commun. Syst. 31(8), e3537 (2018)

    Article  Google Scholar 

  14. Haghshenas, K., Pahlevan, A., Zapater, M., Mohammadi, S., Atienza, D.: MAGNETIC: multi-agent machine learning-based approach for energy efficient dynamic consolidation in data centers. IEEE Trans. Serv. Comput. 15(1), 30–44 (2019)

    Article  Google Scholar 

  15. Laili, Y., Tao, F., Wang, F., Zhang, L., Lin, T.: An iterative budget algorithm for dynamic virtual machine consolidation under cloud computing environment. IEEE Trans. Serv. Comput. 14(1), 30–43 (2018)

    Google Scholar 

  16. Lin, W., Wu, W., He, L.: An on-line virtual machine consolidation strategy for dual improvement in performance and energy conservation of server clusters in cloud data centers. IEEE Trans. Serv. Comput. 15(2), 766–777 (2019)

    Article  Google Scholar 

  17. Mandal, R., Mondal, M.K., Banerjee, S., Srivastava, G., Alnumay, W., Ghosh, U., Biswas, U.: MECpVmS: an SLA aware energy-efficient virtual machine selection policy for green cloud computing. Clust. Comput. (2022). https://doi.org/10.1007/s10586-022-03684-2

    Article  Google Scholar 

  18. Masdari, M., Nabavi, S.S., Ahmadi, V.: An overview of virtual machine placement schemes in cloud computing. J. Netw. Comput. Appl. 66, 106–127 (2016)

    Article  Google Scholar 

  19. Mohammadhosseini, M., Toroghi Haghighat, A., Mahdipour, E.: An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm. J. Supercomput. 75(10), 6904–6933 (2019)

    Article  Google Scholar 

  20. Murtazaev, A., Oh, S.: Sercon: server consolidation algorithm using live migration of virtual machines for green computing. IETE Tech. Rev. 28(3), 212–231 (2011)

    Article  Google Scholar 

  21. Najafizadegan, N., Nazemi, E., Khajehvand, V.: An autonomous model for self-optimizing virtual machine selection by learning automata in cloud environment. Softw. Pract. Exp. 51(6), 1352–1386 (2021)

    Article  Google Scholar 

  22. Park, K., Pai, V.S.: CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper. Syst. Rev. 40(1), 65–74 (2006)

    Article  Google Scholar 

  23. Ruan, X., Chen, H., Tian, Y., Yin, S.: Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds. Future Gener. Comput. Syst. 100, 380–394 (2019)

    Article  Google Scholar 

  24. Saeedi, P., Hosseini Shirvani, M.: An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters. Soft Comput. 25(7), 5233–5260 (2021)

    Article  Google Scholar 

  25. Shaw, R., Howley, E., Barrett, E.: An intelligent ensemble learning approach for energy efficient and interference aware dynamic virtual machine consolidation. Simul. Model. Pract. Theory 102, 101992 (2020)

    Article  Google Scholar 

  26. Shaw, R., Howley, E., Barrett, E.: Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers. Inf. Syst. 107, 101722 (2022)

    Article  Google Scholar 

  27. Standard Performance Evaluation Corporation. http://www.spec.org/

  28. Tabrizchi, H., Kuchaki Rafsanjani, M.: Energy refining balance with ant colony system for cloud placement machines. J. Grid Comput. 19(1), 1–17 (2021)

    Article  Google Scholar 

  29. Tang, M., Pan, S.: A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process. Lett. 41(2), 211–221 (2015)

    Article  Google Scholar 

  30. Teng, F., Yu, L., Li, T., Deng, D., Magoulès, F.: Energy efficiency of VM consolidation in IaaS clouds. J. Supercomput. 73(2), 782–809 (2017)

    Article  Google Scholar 

  31. Tsakalozos, K., Verroios, V., Roussopoulos, M., Delis, A.: Live VM migration under time-constraints in share-nothing IaaS-clouds. IEEE Trans. Parallel Distrib. Syst. 28(8), 2285–2298 (2017)

    Article  Google Scholar 

  32. Voorsluys, W., Broberg, J., Venugopal, S., Buyya, R.: Cost of virtual machine live migration in clouds: a performance evaluation. In: IEEE International Conference on Cloud Computing, 2009, pp. 254–265. Springer (2009)

  33. Wang, J., Yu, J., Zhai, R., He, X., Song, Y.: GMPR: a two-phase heuristic algorithm for virtual machine placement in large-scale cloud data centers. IEEE Syst. J. (2022). https://doi.org/10.1109/JSYST.2022.3187971

    Article  Google Scholar 

  34. Wang, J., Gu, H., Yu, J., Song, Y., He, X., Song, Y.: Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform. J. Cloud Comput. 11, 50 (2022)

    Article  Google Scholar 

  35. Wei, C., Hu, Z.H., Wang, Y.G.: Exact algorithms for energy-efficient virtual machine placement in data centers. Future Gener. Comput. Syst. 106, 77–91 (2020)

    Article  Google Scholar 

  36. Wu, G., Tang, M., Tian, Y.C., Li, W.: Energy-efficient virtual machine placement in data centers by genetic algorithm. In: International Conference on Neural Information Processing, 2012, pp. 315–323. Springer (2012)

  37. Yavari, M., Ghaffarpour Rahbar, A., Fathi, M.H.: Temperature and energy-aware consolidation algorithms in cloud computing. J. Cloud Comput. 8(1), 1–16 (2019)

    Article  Google Scholar 

  38. Zhang, X., Wu, T., Chen, M., Wei, T., Zhou, J., Hu, S., Buyya, R.: Energy-aware virtual machine allocation for cloud with resource reservation. J. Syst. Softw. 147, 147–161 (2019)

    Article  Google Scholar 

  39. Zhao, H., Wang, J., Liu, F., Wang, Q., Zhang, W., Zheng, Q.: Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Trans. Parallel Distrib. Syst. 29(6), 1385–1400 (2018)

    Article  Google Scholar 

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Funding

This work was supported in part by Science and Technology R&D Project of Henan Province (Grant No. 212102210078) and the Key Science and Technology Project of Henan Province (Grant No. 201300210400).

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  1. Junyang Yu has contributed equally to this work.

Authors and Affiliations

  1. School of Software, Henan University, Kaifeng, 475000, Henan, China

    Jinjiang Wang, Junyang Yu, Yixin Song, Xin He & Yalin Song

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  1. Jinjiang Wang
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  2. Junyang Yu
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  3. Yixin Song
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  4. Xin He
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JY, YS and JW. The first draft of the manuscript was written by JW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Junyang Yu.

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Wang, J., Yu, J., Song, Y. et al. An efficient energy-aware and service quality improvement strategy applied in cloud computing. Cluster Comput 26, 4031–4049 (2023). https://doi.org/10.1007/s10586-022-03795-w

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