Nothing Special   »   [go: up one dir, main page]

Skip to main content

Energy Efficient Cloud Data Center Using Dynamic Virtual Machine Consolidation Algorithm

  • Conference paper
  • First Online:
Business Information Systems (BIS 2019)

Abstract

In Cloud Data centers, virtual machine consolidation on minimizing energy consumed aims at reducing the number of active physical servers. Dynamic consolidation of virtual machines (VMs) and switching idle nodes off allow Cloud providers to optimize resource usage and reduce energy consumption. One aspect of dynamic VM consolidation that directly influences Quality of Service (QoS) delivered by the system is to determine the best moment to reallocate VMs from an overloaded or undeloaded host. In this article we focus on energy-efficiency of Cloud datacenter using Dynamic Virtual Machine Consolidation Algorithms by planetLab workload traces, which consists of a thousand PlanetLab VMs with large-scale simulation environments. Experiments are done in a simulated cloud environment by the CloudSim simulation tool. The obtained results show that consolidation reduces the number of migrations and the power consumption of the servers. Also application performances are improved.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alsadie, D., Alzahrani, E.J., Sohrabi, N., Tari, Z., Zomaya, A.Y.: DTFA: a dynamic threshold-based fuzzy approach for power-efficient VM consolidation. In: 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), pp. 1–9. IEEE (2018)

    Google Scholar 

  2. Arroba, P., Moya, J.M., Ayala, J.L., Buyya, R.: Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concurrency Comput. Pract. Experience 29(10), e4067 (2017)

    Article  Google Scholar 

  3. Beloglazov, A., Buyya, R.: Energy efficient allocation of virtual machines in cloud data centers. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 577–578. IEEE (2010)

    Google Scholar 

  4. 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. Concurrency Comput. Pract. Experience 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  5. Challita, S., Paraiso, F., Merle, P.: A study of virtual machine placement optimization in data centers. In: 7th International Conference on Cloud Computing and Services Science, CLOSER 2017, pp. 343–350 (2017)

    Google Scholar 

  6. 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, pp. 135–165. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-62238-5_6

    Chapter  Google Scholar 

  7. Kumar, N., Kumar, R., Aggrawal, M.: Energy efficient DVFS with VM migration. Eur. J. Adv. Eng. Technol. 5(1), 61–68 (2018)

    Google Scholar 

  8. Laili, Y., Tao, F., Wang, F., Zhang, L., Lin, T.: An iterative budget algorithm for dynamic virtual machine consolidation under cloud computing environment (revised December 2017). IEEE Trans. Serv. Comput. (2018)

    Google Scholar 

  9. Nguyen, T.H., Di Francesco, M., Yla-Jaaski, A.: Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Serv. Comput. (2017)

    Google Scholar 

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

    Article  Google Scholar 

  11. Shirvani, M.H., Rahmani, A.M., Sahafi, A.: A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challenges. J. King Saud Univ. Comput. Inf. Sci. (2018)

    Google Scholar 

  12. Shrivastava, A., Patel, V., Rajak, S.: An energy efficient VM allocation using best fit decreasing minimum migration in cloud environment. Int. J. Eng. Sci. 4076 (2017)

    Google Scholar 

  13. Silva Filho, M.C., Monteiro, C.C., Inácio, P.R., Freire, M.M.: Approaches for optimizing virtual machine placement and migration in cloud environments: a survey. J. Parallel Distrib. Comput. 111, 222–250 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatoumata Thiam .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thiam, C., Thiam, F. (2019). Energy Efficient Cloud Data Center Using Dynamic Virtual Machine Consolidation Algorithm. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20485-3_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20484-6

  • Online ISBN: 978-3-030-20485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics