Log in

Energy-efficient cloud data center with fair service level agreement for green computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

More and more cloud data centers provide numerous cloud computing services. However, how to meet customer needs, improve efficiency and reduce costs are important issues that cloud service providers must deal with. For customers, it is very important to consider the quality of service requirements provided by the data center providing public cloud services. Besides, data center operators should consider how to reduce energy consumption. Therefore, for these important issues, we propose a possible balance between service quality and energy conservation strategy. We find the relationship between the minimal service resources and the required level of services. Under conditions consistent with the SLA, our strategy quantifies the quality of service and calculates the required computing resources according to changes in workload to achieve an energy-saving goal. Also, the policy approximate function is derived and can achieve efficient decision-made goals.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. De la Prieta, F., Rodriguez-Gonzalez, S., Chamoso, P., Corchado, J.M., Bajo, J.: Survey of agent-based cloud computing applications. Futur. Gener. Comput. Syst. (2019). https://doi.org/10.1016/j.future.2019.04.037

    Article  Google Scholar 

  2. Sahmim, S., Gharsellaoui, H.: Privacy and security in internet-based computing: cloud computing, internet of things, cloud of things: a review. Proced. Comput. Sci. 112, 1516–1522 (2017). https://doi.org/10.1016/j.procs.2017.08.050

    Article  Google Scholar 

  3. Pedro, R.P.-S., Francisco, J.A.-M., Mariano, A.-C.: Cloud computing (SaaS) adoption as a strategic technology: results of an empirical study. Mob. Inf. Syst. (2017). https://doi.org/10.1155/2017/2536040

    Article  Google Scholar 

  4. Cusumano, M.A.: Technology strategy and management: the cloud as an innovation platform for software development: how cloud computing became a platform. Commun. ACM 62(10), 20 (2019)

    Article  Google Scholar 

  5. Sun, N., Li, Y., Ma, L., Chen, W., Cynthia, D.: Research on cloud computing in the resource sharing system of university library services. Evol. Intel. 12(3), 377 (2019)

    Article  Google Scholar 

  6. Ullah, A., Li, J., Shen, Y., Hussain, A.: A control theoretical view of cloud elasticity: taxonomy, survey and challenges. Clust. Comput. 21(4), 1735–1764 (2018). https://doi.org/10.1007/s10586-018-2807-6

    Article  Google Scholar 

  7. Liu, J., Wang, S., Zhou, A., Xu, J., Yang, F.: SLA-driven container consolidation with usage prediction for green cloud computing. Front. Comput. Sci. 14(1), 42 (2020)

    Article  Google Scholar 

  8. Dimitri, N.: Pricing cloud IaaS computing services. Journal of Cloud Computing (2192–113X) 9(1), 1 (2020).

  9. Sun, Y., Li, X., Mao, Y., Fang, W.: PROXZONE: one cloud computing system for support paas in energy power applications. Intell. Automat. Soft Comput. 23(4), 555 (2017)

    Article  Google Scholar 

  10. Stephen, A., Benedict, S., Kumar, R.P.A.: Monitoring IaaS using various cloud monitors. Clust. Comput. 22(5), 12459 (2019)

    Article  Google Scholar 

  11. Singh, A.K., Sharma, S.D.: High performance computing (HPC) Data center for information as a service (IaaS) security checklist: cloud data governance. Webology 16(2), 83–96 (2019)

    Article  Google Scholar 

  12. Robert, B.: Flexibility-based energy and demand management in data centers: a case study for cloud computing. Energies (2019). https://doi.org/10.3390/en12173301

    Article  Google Scholar 

  13. Luo, W., Tay, W.P., Sun, P., Wen, Y.: On distributed algorithms for cost-efficient data center placement in cloud computing. (2018)

  14. Baig, S.-u.-R.: Data center's telemetry reduction and prediction through modeling techniques. Dissertation/Thesis, Universitat Politècnica de Catalunya, 2019. (2019)

  15. Ganesh Kumar, G., Vivekanandan, P.: Energy efficient scheduling for cloud data centers using heuristic based migration. Clust. Comput. 22, 14073 (2019)

    Article  Google Scholar 

  16. Tang, X., Liao, X., Zheng, J., Yang, X.: Energy efficient job scheduling with workload prediction on cloud data center. Clust. Comput. 21(3), 1581 (2018)

    Article  Google Scholar 

  17. Kashefi, A., Mohammad-Khanli, L., Soltankhah, N.: RP2: a high-performance data center network architecture using projective planes. Clust. Comput. 20(4), 3499 (2017)

    Article  Google Scholar 

  18. Iranmanesh, A., Naji, H.R.: DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing. Clust. Comput. 24(2), 667–681 (2021). https://doi.org/10.1007/s10586-020-03145-8

    Article  Google Scholar 

  19. Li, H., Zhu, G., Zhao, Y., Dai, Y., Tian, W.: Energy-efficient and QoS-aware model based resource consolidation in cloud data centers. Clust. Comput. 20(3), 2793 (2017)

    Article  Google Scholar 

  20. Basmadjian, R.: Flexibility-based energy and demand management in data centers: a case study for cloud computing. Energies 12(17), 3301 (2019)

    Article  Google Scholar 

  21. Qi, W., Li, J., Liu, Y., Liu, C.: Planning of distributed internet data center microgrids. IEEE Trans. Smart Grid 10(1), 762 (2019)

    Article  Google Scholar 

  22. Ahmad, W., Alam, B., Ahuja, S., Malik, S.: A dynamic VM provisioning and de-provisioning based cost-efficient deadline-aware scheduling algorithm for Big Data workflow applications in a cloud environment. Clust. Comput. 24(1), 249–278 (2021). https://doi.org/10.1007/s10586-020-03100-7

    Article  Google Scholar 

  23. Mirsaeid Hosseini, S., Amir Masoud, R., Amir, S.: A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challenges. J. King Saud Univ.: Comput. Informat. Sci. (2020). https://doi.org/10.1016/j.jksuci.2018.07.001

    Article  Google Scholar 

  24. Nasim, R., Zola, E., Kassler, A.J.: Robust optimization for energy-efficient virtual machine consolidation in modern datacenters. Clust. Comput. 21(3), 1681 (2018)

    Article  Google Scholar 

  25. Li, C., Tang, J., Luo, Y.: Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds. Clust. Comput. 21(4), 2013–2029 (2018). https://doi.org/10.1007/s10586-018-2841-4

    Article  Google Scholar 

  26. Jyoti, A., Shrimali, M.: Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust. Comput. 23(1), 377 (2020)

    Article  Google Scholar 

  27. Wei, J., Zeng, X.-F.: Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling. Clust. Comput. 22, 7577 (2019)

    Article  Google Scholar 

  28. Khan, M.A., Paplinski, A., Khan, A.M., Murshed, M., Buyya, R.: Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review. In: Rivera, W. (ed.) Sustainable Cloud and Energy Services: Principles and Practice, pp. 135–165. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  29. Tamilvizhi, T., Parvathavarthini, B.: A novel method for adaptive fault tolerance during load balancing in cloud computing. Clust. Comput. 22(5), 10425 (2019)

    Article  Google Scholar 

  30. Mohammadzadeh, A., Masdari, M., Gharehchopogh, F.S., Jafarian, A.: A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling. Clust. Comput. 24(2), 1479–1503 (2021)

    Article  Google Scholar 

  31. Polepally, V., Shahu Chatrapati, K.: Dragonfly optimization and constraint measure-based load balancing in cloud computing. Clust. Comput. 22(1), 1099 (2019)

    Article  Google Scholar 

  32. Wang, B., Song, Y., Sun, Y., Liu, J.: Analysis model for server consolidation of virtualized heterogeneous data centers providing internet services. Clust. Comput. 22(3), 911 (2019)

    Article  Google Scholar 

  33. Shunfu, J., Chunxia, Y.: An energy-saving strategy based on multi-server vacation queuing theory in cloud data center. J. Supercomput. 74(12), 6766 (2018)

    Article  Google Scholar 

  34. Vila, S., Guirado, F., Lerida, J.L., Cores, F.: Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm. J. Supercomput. 75(3), 1483 (2019)

    Article  Google Scholar 

  35. Panda, S.K., Jana, P.K.: An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Clust. Comput. 22(2), 509 (2019)

    Article  Google Scholar 

  36. Qi, L., Chen, Y., Yuan, Y., Fu, S., Zhang, X., Xu, X.: A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web 23(2), 1275 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ming-Jeng Yang.

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

Yang, MJ. Energy-efficient cloud data center with fair service level agreement for green computing. Cluster Comput 24, 3337–3349 (2021). https://doi.org/10.1007/s10586-021-03342-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03342-z

Keywords

Navigation