Skip to main content

Advertisement

SpringerLink
Log in

Energy-efficient strategy for virtual machine consolidation in cloud environment

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

An important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of existing energy efficiency techniques save energy against the cost on performance degradation. Consolidation techniques leverage thresholds to detect overloaded and underloaded hosts that could be vacated to achieve optimal balance between host utilization and energy consumption. In this research, we propose an energy-efficient strategy (EES) to consolidate virtual machines in cloud environment with an aim of reducing the energy consumption while completing more tasks with the highest throughput. Our proposal makes use of the performance-to-power ratio to set upper thresholds for overload detection. In addition, EES considers the overall data center workload utilization to set lower thresholds, which can reduce the number of virtual machine migrations. The simulation results show that EES leads to energy-efficient workload consolidation with the minimal number of migrations and less energy consumption. The results conclude that EES saves energy consumption without compromising user’s workload requirement.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Amazon EC2 Instance Types. https://aws.amazon.com/ec2/instance-types/

  • Asad Z, Chaudhry MAR (2017) A two-way street: Green big data processing for a greener smart grid. IEEE Syst J 11(2):784–795

    Article  Google Scholar 

  • Barroso LA, Hölzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37

    Article  Google Scholar 

  • 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 

  • Beloglazov A, Buyya R (2013) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379

    Article  Google Scholar 

  • Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74(368):829–836

    Article  MathSciNet  Google Scholar 

  • El Kafhali S, Salah K (2017) Stochastic modelling and analysis of cloud computing data center. In: Proceedings of 20th conference on innovations in clouds, internet and networks, IEEE, pp 122–126

  • El Kafhali S, Salah K (2018a) Performance analysis of multi-Core VMs hosting cloud SaaS applications. Comput Stand Interfaces 55:126–135

    Article  Google Scholar 

  • El Kafhali S, Salah K (2018b) Modeling and analysis of performance and energy consumption in cloud data centers. Arab J Sci Eng 43(12):7789–7802

    Article  Google Scholar 

  • Fan X, Weber W-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th annual international symposium on computer architecture, ACM, pp 13–23

  • Fard SY, Ahmadi MR, Adabi S (2017) A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. J Supercomput 73(10):4347–4368

    Article  Google Scholar 

  • Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H (2015) Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans Serv Comput 8(2):187–198

    Article  Google Scholar 

  • Ghahramani MH, Zhou M, Hon CT (2017) Toward cloud computing QoS architecture: analysis of cloud systems and cloud services. IEEE/CAA J Autom Sin 4(1):6–18

    Article  MathSciNet  Google Scholar 

  • Hanini M, El Kafhali S, Salah K (2019) Dynamic VM allocation and traffic control to manage QoS and energy consumption in cloud computing environment. Int J Comput Appl Technol 60(4):307–316

    Article  Google Scholar 

  • 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 

  • Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G (2008) Power and performance management of virtualized computing environments via lookahead control. In: Proceedings of the international conference on autonomic computing, IEEE, pp 3–12

  • Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G (2009) Power and performance management of virtualized computing environments via lookahead control. Clust Comput 12(1):1–15

    Article  Google Scholar 

  • Li Z, Yan C, Yu L, Yu X (2018) Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener Comput Syst 80:139–156

    Article  Google Scholar 

  • Mobius C, Dargie W, Schill A (2014) Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans Parallel Distrib Syst 25(6):1600–1614

    Article  Google Scholar 

  • 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 

  • Patel N, Patel H (2016) A comprehensive assessment and comparative analysis of simulations tools for cloud computing. Int J Eng Comput Sci 5(11):18972–18978

    Google Scholar 

  • Patel N, Patel H (2017) Energy efficient strategy for placement of virtual machines selected from underloaded servers in compute Cloud. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2017.11.003

    Article  Google Scholar 

  • Rincón D, Agustí-Torra A, Botero JF, Raspall F, Remondo D, Hesselbach X, Beck MT, de Meer H, Niedermeier F, Giuliani G (2013) A novel collaboration paradigm for reducing energy consumption and carbon dioxide emissions in data centres. Comput J 56(12):1518–1536

    Article  Google Scholar 

  • Ruan X, Chen H (2015) Performance-to-power ratio aware virtual machine (VM) allocation in energy-efficient clouds. In: Proceedings of the international conference on cluster computing, IEEE, pp 264–273

  • Song W, Xiao Z, Chen Q, Luo H (2014) Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans Comput 63(11):2647–2660

    Article  MathSciNet  Google Scholar 

  • Standard Performance Evaluation Corporation. https://www.spec.org/power_ssj2008/results/

  • Verma A, Dasgupta G, Nayak TK, De P, Kothari R (2009) Server workload analysis for power minimization using consolidation. In: Proceedings of the 2009 conference on USENIX annual technical conference, ACM, pp 28–28

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

    Article  Google Scholar 

  • Xia Y, Zhou M, Luo X, Zhu Q, Li J, Huang Y (2015) Stochastic modeling and quality evaluation of infrastructure-as-a-service clouds. IEEE Trans Autom Sci Eng 12(1):162–170

    Article  Google Scholar 

  • Xiao X, Zheng W, Xia Y, Sun X, Peng Q, Guo Y (2019) A workload-aware VM consolidation method based on coalitional game for energy-saving in cloud. IEEE Access 7:80421–80430

    Article  Google Scholar 

  • Yuan H, Bi J, Zhou M, Ammari AC (2018) Time-aware multi-application task scheduling with guaranteed delay constraints in green data center. IEEE Trans Autom Sci Eng 15(3):1138–1151

    Article  Google Scholar 

  • Yuan H, Bi J, Zhou M (2019) Spatial task scheduling for cost minimization in distributed green cloud data centers. IEEE Trans Autom Sci Eng 16(2):729–740

    Article  Google Scholar 

  • Younge AJ, Von Laszewski G, Wang L, Lopez-Alarcon S, Carithers W (2010) Efficient resource management for cloud computing environments. In: Proceedings of the international conference on green computing, IEEE, pp 357–364

  • Zhang P, Zhou MC (2018) Dynamic cloud task scheduling based on a two-stage strategy. IEEE Trans Autom Sci Eng 15(2):772–783

    Article  Google Scholar 

  • Zhou Z, Hu ZG, Song T, Yu JY (2015) A novel virtual machine deployment algorithm with energy efficiency in cloud computing. J Central South Univ 22(3):974–983

    Article  Google Scholar 

  • Zhou Z, Zhigang Hu, Li K (2016) Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers. Sci Program 2016:11

    Google Scholar 

Download references

Acknowledgements

The authors thank the anonymous reviewers for their valuable comments, which helped us to considerably improve the content, quality and presentation of this paper.

Author information

Authors and Affiliations

  1. Information Processing and Decision Support Laboratory, Faculty of Sciences and Technologies, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Youssef Saadi

  2. Computer, Networks, Mobility and Modeling Laboratory, Faculty of Sciences and Technologies, Hassan 1st University, Settat, Morocco

    Said El Kafhali

Authors
  1. Youssef Saadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Said El Kafhali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Said El Kafhali.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

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

Saadi, Y., El Kafhali, S. Energy-efficient strategy for virtual machine consolidation in cloud environment. Soft Comput 24, 14845–14859 (2020). https://doi.org/10.1007/s00500-020-04839-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04839-2

Keywords

Search

Navigation