Skip to main content

Advertisement

SpringerLink
Log in

General spin-up time distribution for energy-aware IaaS cloud service models

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing services provided over the Internet are realized by servers in physically distributed data centers that consume tremendous power for operational and maintenance purposes. To minimize energy consumption, modern cloud systems adopt intelligent sever power switching with thresholds based on the current system load and are bounded by the number of idle servers. The time taken to power on physical or virtual servers, known as the spin-up time, can significantly impact the delay incurred in service delivery and elasticity of real cloud platforms. In this paper, we model and assess the asymptotic performance of an energy-aware cloud data center assuming general distribution for server spin-up time. The waiting time of a newly arriving request is defined in the service-level agreement (SLA) and for each busy server, the fixation time distribution derived from an absorbing birth-and-death process characterizes the impact of thresholds. Simulation results show that the proposed model calculates the probability of SLA violation for different threshold values with less than 0.5% error.

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., Merle, P.: Elasticity in cloud computing: state of the art and research challenges. IEEE Trans. Serv. Comput. 11(2), 430–447 (2018)

    Article  Google Scholar 

  3. 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 

  4. Brebner, P.C.: Is your cloud elastic enough? performance modelling the elasticity of infrastructure as a service (IaaS) cloud applications. In: Proceedings of the Third Joint WOSP/SIPEW International Conference on Performance Engineering—ICPE’12. ACM Press, New York, p. 263 (2012)

  5. Nguyen, T.L., Lebre, A.: Virtual Machine Boot Time Model. In: 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 2017, IEEE, pp. 430–437 (2017)

  6. Ashcroft, P., Traulsen, A., Galla, T.: When the mean is not enough: calculating fixation time distributions in birth-death processes. Phys. Rev. E 92(4), 42154 (2015)

    Article  Google Scholar 

  7. 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)

    Google Scholar 

  8. Maccio, V., Down, D.: Structural properties and exact analysis of energy-aware multiserver queueing systems with setup times. Perform. Eval. 121–122, 48–66 (2018)

    Article  Google Scholar 

  9. Mitrani, I.: Service center trade-offs between customer impatience and power consumption. Perform. Eval. 68(11), 1222–1231 (2011)

    Article  Google Scholar 

  10. Hyytiä, E., Down, D., Lassila, P., Aalto, S.: Dynamic control of running servers. In: German, R., Hielscher, K.S., Krieger, U.R. (eds.) Measurement, Modelling and Evaluation of Computing Systems, pp. 127–141. Springer, Cham (2018)

    Chapter  Google Scholar 

  11. Gandhi, A., Harchol-Balter, M., Adan, I.: Server farms with setup costs. Perform. Eval. 67(11), 1123–1138 (2010)

    Article  Google Scholar 

  12. Gandhi, A., Doroudi, S., Harchol-Balter, M., Scheller-Wolf, A.: Exact analysis of the M/M/k/setup class of Markov chains via recursive renewal reward. Queueing Syst. 77(2), 177–209 (2014)

    Article  MathSciNet  Google Scholar 

  13. Phung-Duc, T.: Exact solutions for M/M/c/Setup queues. Telecommun. Syst. 64(2), 309–324 (2017)

    Article  Google Scholar 

  14. Longo, F., Ghosh, R., Naik, V.K., Trivedi, K.S.: A scalable availability model for infrastructure-as-a-service cloud. In: 2011 IEEE/IFIP 41st International Conference on Dependable Systems Networks (DSN), pp. 335–346 (2011)

  15. Wang, B., Chang, X., Liu, J.: Modeling heterogeneous virtual machines on iaas data centers. IEEE Commun. Lett. 19(4), 537–540 (2015)

    Article  Google Scholar 

  16. Chang, X., Wang, B., Muppala, J.K., Liu, J.: Modeling active virtual machines on iaas clouds using an m/g/m/m+k queue. IEEE Trans. Serv. Comput. 9(03), 408–420 (2016)

    Article  Google Scholar 

  17. Gebrehiwot, M.E., Aalto, S., Lassila, P.: Optimal energy-aware control policies for fifo servers. Perform. Eval. 103, 41–59 (2016)

    Article  Google Scholar 

  18. Di, S., Kondo, D., Cappello, F.: Characterizing and modeling cloud applications/jobs on a google data center. J. Supercomput. 69(1), 139–160 (2014)

    Article  Google Scholar 

  19. Devore, J.L.: Probability and Statistics for Engineering and the Sciences. Cengage Learning, Boston (2011)

    Google Scholar 

  20. Howell, F., McNab, R.: Simjava: a discrete event simulation library for java. Simul. Ser. 30, 51–56 (1998)

    Google Scholar 

  21. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 41(1), 23–50 (2011)

    Google Scholar 

  22. Buyya, R., Beloglazov, A., Abawajy, J.: Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements , and Open Challenges Clou d Computing and D istributed S ystems (CLOUDS) Laboratory Department of Computer Science and Software Engineering The. In: PDPTA 2010: Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications, Vm, pp. 1–12, 1006.0308 (2010)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Behzad Chitsaz & Ahmad Khonsari

  2. School of Computer Science, Institute for Research in Fundamental Sciences, Tehran, Iran

    Ahmad Khonsari

Authors
  1. Behzad Chitsaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmad Khonsari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behzad Chitsaz.

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

Chitsaz, B., Khonsari, A. General spin-up time distribution for energy-aware IaaS cloud service models. Cluster Comput 23, 1293–1301 (2020). https://doi.org/10.1007/s10586-019-02993-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-019-02993-3

Keywords

Search

Navigation