[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Prediction-based underutilized and destination host selection approaches for energy-efficient dynamic VM consolidation in data centers

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /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 (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Koomey JG (2007) Estimating total power consumption by servers in the U.S. and the world. Lawrence Berkeley National Laboratory, Stanford University

  2. Shehabi A, Smith S, Sartor D, Brown R, Herrlin M, Koomey J, Masanet E, Horner N, Azevedo I, Lintner W (2016) United states data center energy usage report. Lawrence Berkeley National Laboratory, Berkeley

    Book  Google Scholar 

  3. Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. ACM SIGOPS Oper Syst Rev 37:164–177 ACM

    Article  Google Scholar 

  4. Leelipushpam PGJ, Sharmila J (2013) Live vm migration techniques in cloud environment–a survey. In: 2013 IEEE Conference on Information & Communication Technologies. IEEE, pp 408–413

  5. Sobel W, Subramanyam S, Sucharitakul A, Nguyen J, Wong H, Klepchukov A, Patil S, Fox A, Patterson D (2008) Cloudstone: multi-platform, multi-language benchmark and measurement tools for web 2.0. Proc CA 8:228

    Google Scholar 

  6. Pahlevan A, Qu X, Zapater M, Atienza D (2017) Integrating heuristic and machine-learning methods for efficient virtual machine allocation in data centers. IEEE Trans Comput Aided Des Integr Circuits Syst 37(8):1667–1680

    Article  Google Scholar 

  7. Monil MAH, Rahman RM (2015) Implementation of modified overload detection technique with vm selection strategies based on heuristics and migration control. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), IEEE, pp 223–227

  8. Cao Z, Dong S (2014) An energy-aware heuristic framework for virtual machine consolidation in cloud computing. J Supercomput 69(1):429–451

    Article  Google Scholar 

  9. Beloglazov A, Buyya R (2012) 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Wu Q, Ishikawa F, Zhu Q, Xia Y (2016) Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans Serv Comput 12(4):550–563

    Article  Google Scholar 

  12. Haghshenas K, Pahlevan A, Zapater M, Mohammadi S, Atienza D (2019) Magnetic: Multi-agent machine learning-based approach for energy efficient dynamic consolidation in data centers. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2019.2919555

    Article  Google Scholar 

  13. Farahnakian F, Liljeberg P, Plosila J (2013) Lircup: linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers. In: 2013 39th Euromicro Conference on Software Engineering and Advanced Applications, IEEE, pp 357–364

  14. Melhem SB, Agarwal A, Goel N, Zaman M (2017) Markov prediction model for host load detection and vm placement in live migration. IEEE Access 6:7190–7205

    Article  Google Scholar 

  15. Masoumzadeh SS, Hlavacs H (2013) An intelligent and adaptive threshold-based schema for energy and performance efficient dynamic vm consolidation. In: European Conference On Energy Efficiency in Large Scale Distributed Systems, Springer, pp 85–97

  16. Horri A, Mozafari MS, Dastghaibyfard G (2014) Novel resource allocation algorithms to performance and energy efficiency in cloud computing. J Supercomput 69(3):1445–1461

    Article  Google Scholar 

  17. Nguyen TH, Di Francesco M, Yla-Jaaski A (2017) Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans Serv Comput 13(1):186–199

    Google Scholar 

  18. Khoshkholghi MA, Derahman MN, Abdullah A, Subramaniam S, Othman M (2017) Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access 5:10709–10722

    Article  Google Scholar 

  19. Khan MA, Paplinski AP, Khan AM, Murshed M, Buyya R (2018) Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, pp 105–114

  20. Li Z, Yu X, Yu L, Guo S, Chang V (2020) Energy-efficient and quality-aware vm consolidation method. Future Gener Comput Syst 102:789–809

    Article  Google Scholar 

  21. Ashraf A, Porres I (2018) Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int J Parallel Emerge Distrib Syst 33(1):103–120

    Article  Google Scholar 

  22. Wang H, Tianfield H (2018) Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access 6:15259–15273

    Article  Google Scholar 

  23. Alicherry M, Lakshman T (2013) Optimizing data access latencies in cloud systems by intelligent virtual machine placement. In: 2013 Proceedings IEEE INFOCOM, IEEE, pp 647–655

  24. Sousa S, Martins F, Alvim-Ferraz M, Pereira MC (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22(1):97–103

    Article  Google Scholar 

  25. Seber GA, Lee AJ (2012) Linear regression analysis, vol 329. Wiley, Hoboken

    MATH  Google Scholar 

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

    Google Scholar 

  27. Park K, Pai VS (2006) Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Oper Syst Rev 40(1):65–74

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Tehran, Tehran, 17469-37181, Iran

    Kawsar Haghshenas & Siamak Mohammadi

Authors
  1. Kawsar Haghshenas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siamak Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siamak Mohammadi.

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

Haghshenas, K., Mohammadi, S. Prediction-based underutilized and destination host selection approaches for energy-efficient dynamic VM consolidation in data centers. J Supercomput 76, 10240–10257 (2020). https://doi.org/10.1007/s11227-020-03248-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03248-4

Keywords

Search

Navigation