Skip to main content

Advertisement

SpringerLink
Log in

The \(CiS^2\): a new metric for performance and energy trade-off in consolidated servers

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The increased use of cloud services has turned the virtualization in the main technology that supports cloud datacentres. To reduce the increment of power consumption caused in datacenters due to the addition of physical servers, system administrators are using virtual machine consolidation (VMC) techniques which tries to allocate the adequate number of virtual machines per physical server. Therefore, VMC increases server resources utilization and as a consequence its performance degradation and the energy consumption too. Then, a trade-off between the performance and the energy consumption exists when consolidating virtual machines. This trade-off is difficult to quantify and also to determine the servers efficiency taking into account a specific number of allocated virtual machines. Because of this, it is crucial for system administrators having a simple metric that assists the VMC making-decision process. In this paper, we propose the \(CiS^2\) index, a metric to quantify this performance-energy trade-off. Also, this index can help system administrators to decide about the servers’ efficiency through benchmarking and to select the most efficient server through a proposed algorithm. Besides, we propose a simple graphical representation of the index to distinguish graphically the efficient and non-efficient server consolidations. We validate the index in a theoretical manner and performing real experiments in different physical servers under CPU workload saturation. Obtained results show that the proposed index reflects the performance-energy trade-off behaviour and it helps systems’ administrators when consolidating virtual machines.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Abaunza, F., Hameri, A., Niemi, T.: Eeui: a new measure to monitor and manage energy efficiency in data centers. Int. J. Prod. Perform. Manage. 67(1), 111–127 (2018)

    Article  Google Scholar 

  2. Atrey, A., Jain, N., Iyengar, N.: A study on green cloud computing. Int. J. Grid Distrib. Comput. 6, 93–102 (2013)

    Article  Google Scholar 

  3. Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer 12, 33–37 (2007)

    Article  Google Scholar 

  4. Barroso, L.A., Clidaras, J., Hölzle, U.: The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Arch. 8(3), 1–154 (2013)

    Google Scholar 

  5. Bermejo, B., Filiposka, S., Juiz, C., Gómez, B., Guerrero, C.: Improving the energy efficiency in cloud computing data centres through resource allocation techniques. In: Proceedings of the Research Advances in Cloud Computing, pp. 211–236. Springer (2017)

  6. Bermejo, B., Juiz, C., Guerrero, C.: On the linearity of performance and energy at VMC: the CiS2 index for CPU workload in server saturation. In: Proceedings of the IEEE High Performance Computing and Communications (HPCC-2018) (2018)

  7. Bermejo, B., Juiz, C., Guerrero, C.: Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J. Supercomput. (2018). https://doi.org/10.1007/s11227-018-2613-1

    Article  Google Scholar 

  8. Bermejo, B., Juiz, C., Thomas, N.: On the virtualization overhead and energy consumption in consolidated servers. In: Proceedings of the UK-Performance Engineering Workshop (UKPEW) (2018)

  9. Calheiros, R.N., Ranjan, R., Buyya, R.: Virtual machine provisioning based on analytical performance and qos in cloud computing environments. In: Proceedings of the 2011 International Conference on Parallel Processing (ICPP), pp. 295–304. IEEE (2011)

  10. Cartwright, N., Bradhurn, N.: The possibility of a universal social welfare function. Report, London School of Economics

  11. Casalicchio, E.: A study on performance measures for auto-scaling CPU-intensive containerized applications. Clust. Comput. 22, 995–1006 (2019)

    Article  Google Scholar 

  12. Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)

    Article  Google Scholar 

  13. De Napoli, C., Forestiero, A., Lagana, D., Lupi, G., Mastroianni, C., Spataro, L.: Efficiency and green metrics for distributed data centers. Report P-26, ICAR (2016)

  14. Ferreira, A.M., Pernici, B.: Managing the complex data center environment: an integrated energy-aware framework. Computing 98(7), 709–749 (2016)

    Article  MathSciNet  Google Scholar 

  15. Gonzalez, R., Horowitz, M.: Energy dissipation in general purpose microprocessors. IEEE J. Solid-State Circuits 31(9), 1277–1284 (1996)

    Article  Google Scholar 

  16. Hsu, C.-H., Poole, S.W.: Revisiting server energy proportionality. In: Proceedings of the 2013 42nd International Conference on Parallel Processing (ICPP), pp. 834–840. IEEE (2013)

  17. Huber, N., von Quast, M., Hauck, M., Kounev, S.: Evaluating and modeling virtualization performance overhead for cloud environments. In: Proceedings of the CLOSER, pp. 563–573 (2011)

  18. Hwang, K., Bai, X., Shi, Y., Li, M., Chen, Wen-Guang, Yongwei, Wu: Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans. Parallel Distrib. Syst. 27(1), 130–143 (2016)

    Article  Google Scholar 

  19. Jain, A., Mishra, M., Peddoju, S.K., Jain, N: Energy efficient computing-green cloud computing. In: Proceedings of the 2013 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), pp. 978–982. IEEE (2013)

  20. Jain, R.: The Art of Computer Systems Panalysis: Techniques for Experimental Dmeasurement, Simulation, and Modeling. Wiley, Boca Raton (1990)

    Google Scholar 

  21. Jiang, C., Wang, Y., Ou, D., Li, Y., Zhang, J., Wan, J., Shi, W.: Energy efficiency comparison of hypervisors. In: Proceedings of the Sustainable Computing: Informatics and Systems (2017)

  22. Kaur, T., Chana, I.: Energy efficiency techniques in cloud computing: a survey and taxonomy. ACM Comput. Surv. (CSUR) 48(2), 22 (2015)

    Article  Google Scholar 

  23. Kueng, P.: Process performance measurement system: a tool to support process-based organizations. Total Qual. Manage. 11(1), 67–85 (2000)

    Article  Google Scholar 

  24. Lovász, G., Niedermeier, F., De Meer, H.: Performance tradeoffs of energy-aware virtual machine consolidation. Clust. Comput. 16(3), 481–496 (2013)

    Article  Google Scholar 

  25. Minas, L., Ellison, B.: Energy Efficiency for Information Technology: How to Reduce Power Consumption in Servers and Data Centers. Intel Press, Hillsboro (2009)

    Google Scholar 

  26. Molero, X., Juiz, C., Rodeño, M.J.: Evaluación y modelado del rendimiento de los sistemas informáticos. Prentice Hall, Upper Saddle River (2004)

  27. Monteiro, A., Loques, O.: Quantum virtual machine: power and performance management in virtualized web servers clusters. Clust. Comput. 22(1), 205–221 (2019)

    Article  Google Scholar 

  28. Munteanu, I., Debusschere, V., Bergeon, S., Bacha, S.: Efficiency metrics for qualification of datacenters in terms of useful workload. In: Proceedings of the PowerTech (POWERTECH), 2013 IEEE Grenoble, pp. 1–6. IEEE (2013)

  29. Muraña, J., Nesmachnow, S., Armenta, F., Tchernykh, A.: Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores. Clust. Comput. (2019). https://doi.org/10.1007/s10586-018-2882-8

    Article  Google Scholar 

  30. Panda, S.K., Jana, P.K.: An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Clust. Comput. 22, 509–527 (2018)

    Article  Google Scholar 

  31. Prakash, S.J., Subramanyam, K., Prasad, U.D.S.V.: Towards energy efficiency of green computing based on virtualization. Int. J. Emerg. Trends Eng. Dev. 7(2), 415–423 (2012)

    Google Scholar 

  32. Reddy, V.D., Setz, B., Rao, G.S.V., Gangadharan, G.R., Aiello, M.: Metrics for sustainable data centers. IEEE Trans. Sustain. Comput. 2(3), 290–303 (2017)

    Article  Google Scholar 

  33. Reddy, V.D., Setz, B., Rao, G.S.V., Gangadharan, G.R., Aiello, M.: Live migration in bare-metal clouds. IEEE Trans. Cloud Comput. 1(1), 99 (2018)

    Google Scholar 

  34. Sen, R., Wood, D.A.: Energy-proportional computing: a new definition. Computer 8, 26–33 (2017a)

    Article  Google Scholar 

  35. Sen, R., Wood, D.A.: Pareto governors for energy-optimal computing. ACM Trans. Arch. Code Optim. (TACO) 14(1), 6 (2017b)

    Google Scholar 

  36. Tang, C.-J., Dai, M.-R., He, H.-C., Chuang, C.-C.: Evaluating energy efficiency of data centers with generating cost and service demand. Bull. Netw. Comput. Syst. Softw. 1(1), 16 (2012)

    Google Scholar 

  37. Uddin, M., Rahman, A.A.: Server consolidation: an approach to make data centers energy efficient and green. arXiv Preprint. arXiv:1010.5037 (2010)

  38. Uddin, M., Rahman, A.A.: Energy efficiency and low carbon enabler green it framework for data centers considering green metrics. Renew. Sustain. Energy Rev. 16(6), 4078–4094 (2012)

    Article  Google Scholar 

  39. Vasan, A., Sivasubramaniam, A., Shimpi, V., Sivabalan, T., Subbiah, R.: Worth their watts? An empirical study of datacenter servers. In: Proceedings of the 2010 IEEE 16th International Symposium on High Performance Computer Architecture (HPCA), pp. 1–10. IEEE (2010)

  40. Ventre, P.L., Lungaroni, P., Siracusano, G., Pisa, C., Schmidt, F., Lombardo, F., Salsano, S.: On the fly orchestration of unikernels: tuning and performance evaluation of virtual infrastructure managers. IEEE Trans. Cloud Comput. (2018)

  41. Volk, E., Tenschert, A., Gienger, M., Oleksiak, A., Sisó, L., Salom, J.: Improving energy efficiency in data centers and federated cloud environments: comparison of coolemall and eco2clouds approaches and metrics. In: Proceedings of the 2013 Third International Conference on Cloud and Green Computing (CGC), pp. 443–450. IEEE (2013)

  42. von Kistowski, J., Block, H., Beckett, J., Spradling, C., Lange, K.-D., Kounev, S.: Variations in CPU power consumption. In: Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering, pp. 147–158. ACM (2016)

  43. Wang, B., Song, Y., Sun, Y., Liu, J.: Analysis model for server consolidation of virtualized heterogeneous data centers providing internet services. Clust. Comput. 22, 911–928 (2018)

    Article  Google Scholar 

  44. Wang, L., Khan, S.U.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomput. 63(3), 639–656 (2013)

    Article  Google Scholar 

  45. Whitney, J., Delforge, P.: Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers. Issue Paper No. IP, pp. 14–08 (2014)

  46. Xu, C., Ma, X., Shea, R., Wang, H., Liu, J.: Enhancing performance and energy efficiency for hybrid workloads in virtualized cloud environment. IEEE Trans. Cloud Comput. (2018). https://doi.org/10.1109/TCC.2018.2837040

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Hasso-Plattner Institut who provided part of the infrastructure to perform the experimentation needed for this work through the WEEVIL2 project.

Author information

Authors and Affiliations

  1. Computer Science Department, University of the Balearic Islands, Palma de Mallorca, Spain

    Carlos Juiz & Belen Bermejo

Authors
  1. Carlos Juiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Belen Bermejo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Belen Bermejo.

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

Juiz, C., Bermejo, B. The \(CiS^2\): a new metric for performance and energy trade-off in consolidated servers. Cluster Comput 23, 2769–2788 (2020). https://doi.org/10.1007/s10586-019-03043-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-019-03043-8

Keywords

Search

Navigation