Skip to main content

The Poisson Cluster Process Runs as a Model for the Internet Traffic

  • Conference paper
Book cover Smart Spaces and Next Generation Wired/Wireless Networking (ruSMART 2009, NEW2AN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 5764))

  • 731 Accesses

Abstract

In this paper, we investigated a Poisson cluster process as runs of packet arrivals on the Internet. This model assumes that the Poisson cluster process is characterized by runs of packets which correspond to defined clusters in the Poisson process. Using the form of the length runs we studied the probability of a general number of cluster runs in the data stream. We illustrated how the obtained results can be used for the analysis of the real-life Internet traffic.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Barlett, M.S.: The Spectral Analysis of Point Processes. Journal of Royal Statistical Society, Series B 25, 264–296 (1963)

    MathSciNet  Google Scholar 

  2. Crovella, M., Bestavros, A.: Self-similarity in World Wide Web Traffic: Evidence and Possible Causes. In: Proc. of the 1996 ACM SIGMETRICS Int. Conf. on Measurement and Modeling of Computer Systems, vol. 24, pp. 160–169 (1996)

    Google Scholar 

  3. Crovella, M., Bestavros, A., Taqqu, M.S.: Heavy-Tailed Probability Distributions in the World Wide Web (1996)

    Google Scholar 

  4. Daley, D.: Asymptotic Properties of Stationary Point Processes with Generalized Clusters. Zeitschrift für Wahrscheinlichkeistheorie und verwandte Gebiete 21, 65–76 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  5. Daley, D., Vere-Jones, D.: An Introduction to the Theory of Point Processes. Springer, Heidelberg (1988)

    MATH  Google Scholar 

  6. Fang-Hao, K.M., Lekshman, T.W., Mohanty, S.: Fast, Memory Efficient Flow Rate Estimation Using Runs. IEEE/ACM Transactions on Networking 15(6), 1467–1477 (2007)

    Article  Google Scholar 

  7. Faÿ, G., Gonzales-Arélo, B., Mikosh, T., Samorodinsky, G.: Modeling Teletraffic Arrivals by a Poisson Cluster Process. Queueing Systems 54, 121–140 (2006)

    Article  MathSciNet  Google Scholar 

  8. Fisz, M.: Probability Theory and Mathematical Statistics. John Wiley & Sons, Chichester (1963)

    MATH  Google Scholar 

  9. Heath, D., Resnick, S., Samorodnitsky, G.: Heavy Tails and Long Range Dependences in ON/OFF Processes and Associated Fluid Models. Mathematical Operation Research 23, 145–165 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hohn, N., Veitch, D.: Inverting Sampled Traffic. In: ACM SIGCOMM Internet Measurement Conference, pp. 222–233 (2003)

    Google Scholar 

  11. Hohn, N., Veitch, D., Abry, P.: Cluster Processes: A Natural Language for Network Traffic. IEEE Trans. on Signal Processing 51, 2229–2244 (2003)

    Article  MathSciNet  Google Scholar 

  12. Kulkarni, L.A.: Transient Behaviour of Queueing Systems with Correlated Traffic. Performance Evaluation 27-28, 117–146 (1996)

    Article  MATH  Google Scholar 

  13. Kodialam, M., Lakshman, T.V., Mohanty, S.: Runs Based Traffic Estimator (RATE): A Simple, Memory Efficient Scheme for Per-Flow Rate Estimation. In: IEEE INFOCOM, vol. 3, pp. 1808–1818 (2004)

    Google Scholar 

  14. Latouche, G., Remiche, M.-A.: An MAP-Based Poisson Cluster Model for Web Traffic. Performance Evaluation 49(1), 359–370 (2002)

    Article  MATH  Google Scholar 

  15. Lewis, P.A.W.: A Branching Poisson Process Model for the Analysis of Computer Failure Patterns. Journal of Royal Statistic Society, Series B 26, 398–456 (1964)

    MathSciNet  MATH  Google Scholar 

  16. Paxson, V., Floyd, S.: Wide-Area Traffic: the Failure of Poisson Modeling. IEEE/ACM Trans. on Networking 3(3), 226–244 (1995)

    Article  Google Scholar 

  17. Sohraby, K.: Delay Analysis of a Single Server Queue with Poisson Cluster Arrival Process Arising in ATM Networks. In: IEEE Global Telecommunication Conference, GLOBECOM 1989, vol. 1, pp. 611–616 (1989)

    Google Scholar 

  18. Westcott, M.: On Existence and Mixing Results for Cluster Point Processes. Journal of Royal Statistics Society, Series B 33, 290–300 (1971)

    MathSciNet  MATH  Google Scholar 

  19. Wald, A., Wolfowitz, J.: On a Test Whether Two Samples are From the Same Population, vol. 11. AMS (1940)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martyna, J. (2009). The Poisson Cluster Process Runs as a Model for the Internet Traffic. In: Balandin, S., Moltchanov, D., Koucheryavy, Y. (eds) Smart Spaces and Next Generation Wired/Wireless Networking. ruSMART NEW2AN 2009 2009. Lecture Notes in Computer Science, vol 5764. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04190-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04190-7_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04188-4

  • Online ISBN: 978-3-642-04190-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics