Skip to main content

Abstract

Cloud computing is a computing paradigm in which different computing resources, including infrastructure, hardware platforms, and software applications, are made accessible to remote users as services. Successful provision of infrastructure-as-a-service (IaaS) and, consequently, widespread adoption of cloud computing necessitates accurate performance evaluation that allows service providers to dimension their resources in order to fulfil the service level agreements with their customers. In this paper, we describe an analytical model for performance evaluation of cloud server farms, and demonstrate the manner in which important performance indicators such as request waiting time and server utilization may be assessed with sufficient accuracy.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boxma, O.J., Cohen, J.W., Huffel, N.: Approximations of the mean waiting time in an M/G/s queueing system. Operations Research 27, 1115–1127 (1979)

    Article  MATH  Google Scholar 

  2. Hokstad, P.: Approximations for the M/G/m queues. Operations Research 26, 510–523 (1978)

    Article  MATH  Google Scholar 

  3. Kimura, T.: Diffusion approximation for an M/G/m queue. Operations Research 31, 304–321 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kleinrock, L.: Queueing Systems. Theory, vol. 1. Wiley-Interscience, Hoboken (1975)

    MATH  Google Scholar 

  5. Ma, B.N.W., Mark, J.W.: Approximation of the mean queue length of an M/G/c queueing system. Operations Research 43, 158–165 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  6. Maplesoft, Inc. Maple 13. Waterloo, ON, Canada (2009)

    Google Scholar 

  7. Miyazawa, M.: Approximation of the queue-length distribution of an M/GI/s queue by the basic equations. J. Applied Probability 23, 443–458 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  8. Nozaki, S.A., Ross, S.M.: Approximations in finite-capacity multi-server queues with poisson arrivals. J. Applied Probability 15, 826–834 (1978)

    MathSciNet  MATH  Google Scholar 

  9. Page, E.: Tables of waiting times for M/M/n, M/D/n and D/M/n and their use to give approximate waiting times in more general queues. J. Operational Research Society 33, 453–473 (1982)

    Article  MATH  Google Scholar 

  10. RSoft Design. Artifex v.4.4.2. RSoft Design Group, Inc., San Jose, CA (2003)

    Google Scholar 

  11. searchcloudcomputing.techtarget.com. Cloud computing definition (2010), http://searchcloudcomputing.techtarget.com/sDefinition/0,,sid201_gci128 7881,00.html

    Google Scholar 

  12. Takagi, H.: Queuing Analysis. Vacation and Priority Systems, part 1, vol. 1. Elsevier Science Publisher B.V., Amsterdam (1991)

    Google Scholar 

  13. Takahashi, Y.: An approximation formula for the mean waiting time of an M/G/c queue. J. Operational Research Society 20, 150–163 (1977)

    MathSciNet  MATH  Google Scholar 

  14. Tijms, H.C., Hoorn, M.H.V., Federgru, A.: Approximations for the steady-state probabilities in the M/G/c queue. Advances in Applied Probability 13, 186–206 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  15. Vaquero, L., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1) (2009)

    Google Scholar 

  16. Wang, L., Laszewski, G.V., Younge, A., He, X., Kunze, M., Tao, J., Fu, C.: Cloud computing: a perspective study. New Generation Computing 28, 137–146 (2010)

    Article  MATH  Google Scholar 

  17. Xiong, K., Perros, H.: Service performance and analysis in cloud computing, Los Alamitos, CA, USA, pp. 693–700 (2009)

    Google Scholar 

  18. Yang, B., Tan, F., Dai, Y., Guo, S.: Performance evaluation of cloud service considering fault recovery. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing. LNCS, vol. 5931, pp. 571–576. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  19. Yao, D.D.: Refining the diffusion approximation for the M/G/m queue. Operations Research 33, 1266–1277 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  20. Yigitbasi, N., Iosup, A., Epema, D., Ostermann, S.: C-meter: A framework for performance analysis of computing clouds. In: CCGRID 2009: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, Washington, DC, USA, pp. 472–477 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Khazaei, H., Mišić, J., Mišić, V.B. (2012). Performance Analysis of Cloud Computing Centers. In: Zhang, X., Qiao, D. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29222-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29222-4_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29221-7

  • Online ISBN: 978-3-642-29222-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics