Skip to main content
SpringerLink
Log in

A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

Cloud Computing has revolutionized the software, platform and infrastructure provisioning. Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual Machine (VMs) to tenants of cloud computing services. A key consolidation force that widespread IaaS deployment is the use of pay-as-you-go and pay-as-you-use cost models. In these models, a service price can be composed of two dimensions: the individual consumption, and a proportional value charged for service maintenance. A common practice for public providers is to dilute both capital and operational costs on predefined pricing sheets. In this context, we propose PSVE (Proportional-Shared Virtual Energy), a cost model for IaaS providers based on CPU energy consumption. Aligned with traditional commodity prices, PSVE is composed of two key elements: an individualized cost accounted from CPU usage of VMs (e.g., processing and networking), and a shared cost from common hypervisor management operations, proportionally distributed among VMs.

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.

Similar content being viewed by others

References

  1. Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I.: Cloud computing pricing models: a survey. Int. J. Grid Distributed Comput. 6(5), 93–106 (2013)

    Article  Google Scholar 

  2. Aldossary, M., Djemame, K.: Energy consumption-based pricing model for cloud computing, September 2016. This is an author produced version of a paper given at and published in 32nd UK Performance Engineering Workshop

  3. Avelar, V., Azevedo, D., French, A.: Pue (tm): A Comprehensive Examination of the Metric. Technical report, The Green Grid (2012)

  4. Barham, P, Dragovic, B, Fraser, K, Hand, S, Harris, T, Ho, A, Neugebauer, R, Pratt, I, Warfield, A: Xen and the art of virtualization. SIGOPS Oper. Syst. Rev. 37(5), 164–177 (2003). https://doi.org/10.1145/1165389.945462

    Article  Google Scholar 

  5. Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer (2007)

  6. Begum, S., Khan, M.K.: Potential of cloud computing architecture. In: 2011 International Conference on Information and Communication Technologies (ICICT), pp. 1–5. IEEE (2011)

  7. Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 826–831. IEEE Computer Society (2010)

  8. Chawla, C., Chana, I.: Strategy-proof pricing approach for cloud market. arXiv:1506.06648 [cs] (2015)

  9. Cheng, L., Rao, J., Lau, F.C.M.: vScale: automatic and efficient processor scaling for SMP virtual machines. In: Proceedings of the Eleventh European Conference on Computer Systems, EuroSys ’16, pp. 2:1–2:14, ACM, New York (2016)

  10. Coroama, V., Hilty, L.M.: Energy consumed vs. energy saved by ICT - a closer look. In: 23rd Int. Conf. on Informatics for Environmental Protection (2009)

  11. Cronkite-Ratcliff, B., Bergman, A., Vargaftik, S., Ravi, M., McKeown, N., Abraham, I., Keslassy, I.: Virtualized congestion control. In: Proceedings of the 2016 Conference on ACM SIGCOMM 2016 Conference, SIGCOMM ’16, pp. 230–243. ACM, New York (2016)

  12. David, M.P., Schmidt, R.R.: Impact of ashrae environmental classes on data centers. In: Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2014 IEEE, 09 (2014)

  13. Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: A Survey. IEEE Commun. Surv. Tutorials 18(1), 732–794 (2016)

    Article  Google Scholar 

  14. ENP: Energy logic: Reducing data center energy consumption by creating savings that cascade across systems (2008)

  15. Feller, E., Rohr, C., Margery, D., Morin, C.: Energy management in Iaas clouds: a holistic approach. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 204–212. IEEE (2012)

  16. Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)

    Article  Google Scholar 

  17. García García, A., Blanquer, I.: Cloud services representation using SLA composition. J. Grid Comput. 13(1), 35–51 (2015)

    Article  Google Scholar 

  18. Gmach, D., Rolia, J., Cherkasova, L.: Resource and virtualization costs up in the cloud: models and design choices. In: Proc. 41St IEEE/IFIP DSN (2011)

  19. Han, Y.: Cloud computing: case studies and total cost of ownership. Information Technology & Libraries (2011)

  20. He, K., Rozner, E., Agarwal, K., Gu, Y. (Jason), Felter, W., Carter, J., Akella, A.: Ac/dc Tcp: virtual congestion control enforcement for datacenter networks. In: Proceedings of the 2016 Conference on ACM SIGCOMM 2016 conference, SIGCOMM ’16, pp. 244–257. New York, ACM . https://doi.org/10.1145/2934872.2934903 (2016)

  21. Iyengar, M., Schmidt, R., Caricari, J.: Reducing energy usage in data centers through control of room air conditioning units. In: Proceedings of the IEEE ITherm Conference in Las Vegas, pp. 1–11, 07 (2010)

  22. Kansal, N.J., Chana, I.: Energy-aware virtual machine migration for cloud computing - a firefly optimization approach. J. Grid Comput. 14(2), 327–345 (2016)

    Article  Google Scholar 

  23. Kansal, S., Singh, G., Kumar, H., Kaushal, S.: Pricing models in cloud computing. In: Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, ICTCS ’14, pp. 33:1–33:5. ACM, New York (2014)

  24. Kertesz, A., Dombi, J.D., Benyi, A.: A pliant-based virtual machine scheduling solution to improve the energy efficiency of iaas clouds. J. Grid Comput. 14(1), 41–53 (2016)

    Article  Google Scholar 

  25. Koomey, J.G.: Worldwide electricity used in data centers. Environ. Res. Lett. 3(3), 034008 (2008)

    Article  Google Scholar 

  26. Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)

    Article  Google Scholar 

  27. Li, S., Lim, K., Faraboschi, P., Chang, J., Ranganathan, P., Jouppi, N.P.: System-level integrated server architectures for scale-out datacenters. In: Proc. 44th IEEE/ACM MICRO (2011)

  28. Leong, B.G.L., Toombs, D.: Magic quadrant for cloud infrastructure as a service, worldwide. Gartner RAS Core Research (2015)

  29. Mach, W., Schikuta, E.: Toward an economic and energy-aware cloud cost model. Concurrency Comput. Prac. Exp. 25(18), 2471–2487 (2013)

    Article  Google Scholar 

  30. Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.-M., Vasilakos, A.V.: Cloud computing: Survey on energy efficiency. ACM Comput. Surv. 47(2), 33:1–33:36 (2014)

    Article  Google Scholar 

  31. Meisner, D., Gold, B.T., Wenisch, Thomas F: Powernap: eliminating server idle power. In: ACM Sigplan Notices, vol. 44. ACM (2009)

  32. Mell, P., Grance, T.: The NIST definition of cloud computing. In: Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology (2011)

  33. Mobius, C., Dargie, W., Schill, A.: Power consumption estimation models for processors, virtual machines, and servers. In: IEEE Transactions on Parallel and Distributed Systems (2014)

  34. Popek, G.J., Goldberg, R.P.: Formal requirements for virtualizable third generation architectures. Commun. ACM 17(7), 412–421 (1974). https://doi.org/10.1145/361011.361073

    Article  MathSciNet  MATH  Google Scholar 

  35. Reza Rahimi, M., Ren, J., Liu, C.H., Vasilakos, A.V., Venkatasubramanian, N.: Mobile cloud computing: a survey, state of art and future directions. Mobile Netw. Appl. 19(2), 133–143 (2014)

    Article  Google Scholar 

  36. Rimal, B.P., Jukan, A., Katsaros, D., Goeleven, Y.: Architectural requirements for cloud computing systems: an enterprise cloud approach. J. Grid Comput. 9(1), 3–26 (2011)

    Article  Google Scholar 

  37. Ruck, D., Miers, C., Pillon, M., Koslovski, G.: Eavira: Energy-aware virtual infrastructure reallocation algorithm. In: 2017 VII Brazilian Symposium on Computing Systems Engineering, SBESC (2017)

  38. Sharifi, L., Cerdà-Alabern, L., Freitag, F., Veiga, L.: Energy efficient cloud service provisioning keeping data center granularity in perspective. J. Grid Comput. 14(2), 299–325 (2016)

    Article  Google Scholar 

  39. Shuja, J., Bilal, K., Madani, S.A., Khan, S.U.: Data center energy efficient resource scheduling. Clust. Comput. 17(4), 1265–1277 (2014). https://doi.org/10.1007/s10586-014-0365-0

    Article  Google Scholar 

  40. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016)

    Article  Google Scholar 

  41. Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Proc. of the Conf. on Power Aware Computing and Systems. USENIX (2008)

  42. Srinivasan, M.K., Sarukesi, K., Rodrigues, P., Sai Manoj, M., Revathy, P.: State-Of-The-Art cloud computing security taxonomies: a classification of security challenges in the present cloud computing environment. In: Proc. ICACCI’12, ACM (2012)

  43. Stallings, W.: Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud. 1st edn. Addison-Wesley Professional, Boston (2015)

  44. Tang, S., Lee, B.-S., He, B., Liu, H.: Long-term resource fairness: towards economic fairness on Pay-As-You-Use computing systems. In: Proc. of the 28Th ACM Int. Conf. on Supercomputing (2014)

  45. Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-Optimal scheduling in hybrid Iaas clouds for deadline constrained workloads. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 228–235. IEEE (2010)

  46. Verma, A., Kaushal, S.: Cost-time efficient scheduling plan for executing workflows in the cloud. J. Grid Comput. 13(4), 495–506 (2015)

    Article  MathSciNet  Google Scholar 

  47. Versick, D., Wassmann, I., Tavangarian, D.: Power consumption estimation of cpu and peripheral components in virtual machines. SIGAPP Appl. Comput. Rev. 13(3), 17–25 (2013). https://doi.org/10.1145/2537728.2537730

    Article  Google Scholar 

  48. Primet, P.V.-B., Anhalt, F., Koslovski, G.: Exploring the virtual infrastructure service concept in grid’5000. In: 20th ITC Specialist Seminar on Network Virtualization. Hoi An, Vietnam (2009)

  49. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank LabP2D (http://labp2d.joinville.udesc.br) for providing the testbed resources and technical support, and the Santa Catarina State University (UDESC) research funding program.

Author information

Authors and Affiliations

  1. Brazil, IBM, Porto Alegre, RS, Brazil

    Mauro Hinz

  2. Graduate Program in Applied Computing, Santa Catarina State University, Joinville, SC, Brazil

    Guilherme Piegas Koslovski, Charles C. Miers & Maurício A. Pillon

  3. Department of Computer Science, Federal University of Santa Catarina, Florianópolis, SC, Brazil

    Laércio L. Pilla

Authors
  1. Mauro Hinz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guilherme Piegas Koslovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charles C. Miers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laércio L. Pilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maurício A. Pillon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guilherme Piegas Koslovski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hinz, M., Koslovski, G.P., Miers, C.C. et al. A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption. J Grid Computing 16, 493–512 (2018). https://doi.org/10.1007/s10723-018-9440-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-018-9440-8

Keywords

Search

Navigation