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Measurement and Analysis of Intraflow Performance Characteristics of Wireless Traffic

  • Dimitrios P. Pezaros
  • Manolis Sifalakis
  • David Hutchison
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4786)

Abstract

It is by now widely accepted that the arrival process of aggregate network traffic exhibits self-similar characteristics which result in the preservation of traffic burstiness (high variability) over a wide range of timescales. This behaviour has been structurally linked to the presence of heavy-tailed, infinite variance phenomena at the level of individual network connections, file sizes, transfer durations, and packet inter-arrival times. In this paper, we have examined the presence of fractal and heavy-tailed behaviour in a number of performance aspects of individual IPv6 microflows as routed over wireless local and wide area network topologies. Our analysis sheds light on several questions regarding flow-level traffic behaviour: whether burstiness preservation is mainly observed at traffic aggregates or is it also evident at individual microflows; whether it is influenced by the end-to-end transport control mechanisms as well as by the network-level traffic multiplexing; whether high variability is independent from diverse link-level technologies, and whether burstiness is preserved in end-to-end performance metrics such as packet delay as well as in the traffic arrival process. Our findings suggest that traffic and packet delay exhibit closely-related Long-Range Dependence (LRD) at the level of individual microflows, with marginal to moderate intensity. Bulk TCP data and UDP flows produce higher Hurst exponent estimates than the acknowledgment flows that consist of minimum-sized packets. Wireless access technologies seem to also influence LRD intensity. At the same time, the distributions of intraflow packet inter-arrival times do not exhibit infinite variance characteristics.

Keywords

LRD Hurst exponent heavy-tailed distribution ACF 

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References

  1. 1.
    Abry, P., Veitch, D.: Wavelet Analysis of Long-Range Dependence Traffic. In: IEEE Transactions on Information Theory, IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  2. 2.
    Beran, J.: Statistics for Long-Memory Processes. In: Monographs on Statistics and Applied Probability, Chapman and Hall, New York (1994)Google Scholar
  3. 3.
    Borella, M.S., Brewster, G.B.: Measurement and Analysis of Long-Range Dependent Behaviour of Internet Packet Delay. In: Proceedings, IEEE Infocom 1998, pp. 497–504 (April 1998)Google Scholar
  4. 4.
    Brownlee, N., Claffy, K.C.: Understanding Internet Traffic Streams: Dragonflies and Tortoises. IEEE Communications Magazine 40(10), 110–117 (2002)CrossRefGoogle Scholar
  5. 5.
    Chakravorty, R., Cartwright, J., Pratt, I.: Practical experience with TCP over GPRS. In: IEEE GLOBECOM 2002, Taiwan (2002)Google Scholar
  6. 6.
    Crovella, M.E., Bestavros, A.: Self-similarity in World Wide Web traffic: Evidence and possible causes. IEEE/ACM Trans. on Networking 5(6), 835–846 (1997)CrossRefGoogle Scholar
  7. 7.
    Delignieres, D., Ramdani, S., Lemoine, L., Torre, K., Fortes, M., Ninot, G.: Fractal analyses for ‘short’ time series: a re-assessment of classical methods. Journal of Mathematical Psychology 50 (2006)Google Scholar
  8. 8.
    Downey, A.B.: Lognormal and Pareto distributions in the Internet. Computer Communications 28(7), 790–801 (2005)CrossRefGoogle Scholar
  9. 9.
    Erramilli, E., Narayan, O., Willinger, W.: Experimental queuing analysis with long-range dependent packet traffic. IEEE/ACM Trans. on Networking 4(2), 209–223 (1996)CrossRefGoogle Scholar
  10. 10.
    Fotiadis, G., Siris, V.: Improving TCP throughput in 802.11 WLANs with high delay variability. In: ISWCS 2005. 2nd IEEE Int’l Symposium on Wireless Communication Systems, Italy (2005)Google Scholar
  11. 11.
    Hannig, J., Marron, J.S., Samorodnitsky, G., Smith, F.D.: Log-normal durations can give long range dependence. In: Mathematical Statistics and Applications: Festschrift for Constance van Eeden, IMS Lecture Notes, Monograph Series, Institute of Mathematical Statistics, pp. 333-344 (2001)Google Scholar
  12. 12.
    Karagiannis, T., Molle, M., Faloutsos, M.: Understanding the limitations of estimation methods for long-range dependence, Technical Report, University of California, Riverside, TR UCR-CS-2006-10245 (2006)Google Scholar
  13. 13.
    Leland, W.E., Taqqu, M., Willinger, W., Wilson, D.V.: On the Self-Similar Nature of Ethernet Traffic (extended version. IEEE/ACM Trans. on Networking 2(1), 1–15 (1994)CrossRefGoogle Scholar
  14. 14.
    Li, Q., Mills, D.L.: On the long-range dependence of packet round-trip delays in internet. In: Proceedings of IEEE ICC 1998, IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  15. 15.
    Park, K., Kim, G., Crovella, M.: On the Relationship Between File Sizes, Transport Protocols, and Self-Similar Network Traffic. In: IEEE International Conference on Network Protocols ICNP 1996, Ohio, USA, October 29- November 1, 1996, IEEE, Los Alamitos (1996)Google Scholar
  16. 16.
    Park, K., Willinger, W. (eds.): Self-Similar Network Traffic and Performance Evaluation. John Wiley & Sons, New York (2000)Google Scholar
  17. 17.
    Paxson, V., Floyd, S.: Wide-area traffic: The failure of Poisson modelling. IEEE/ACM Trans. on Networking 3(3), 226–244 (1994)CrossRefGoogle Scholar
  18. 18.
    Pezaros, D.P., Hutchison, D., Garcia, F.J., Gardner, R.D., Sventek, J.S.: In-line Service Measurements: An IPv6-based Framework for Traffic Evaluation and Network Operations. In: IEEE/IFIP Network Operations and Management Symposium NOMS 2004, Seoul, Korea (April 29-23, 2004)Google Scholar
  19. 19.
    Pezaros, D.P., Sifalakis, M., Hutchison, D.: End-To-End Microflow Performance Measurement of IPv6 Traffic Over Diverse Wireless Topologies. In: Wireless Internet Conference WICON 2006, Boston, MA (August 2-5, 2006)Google Scholar
  20. 20.
    Pezaros, D.P., Sifalakis, M., Mathy, L.: Fractal Analysis of Intraflow Unidirectional Delay over W-LAN and W-WAN Environments. In: Proceedings of the third International Workshop on Wireless Network Measurement (WiNMee 2007) and on Wireless Traffic Measurements and Modelling (WiTMeMo 2007), Limassol, Cyprus (April 20, 2007)Google Scholar
  21. 21.
    Taqqu, M.S., Teverovsky, V., Willinger, W.: Estimators for long-range dependence: An empirical study. Fractals 3(4), 785–798 (1995)zbMATHCrossRefGoogle Scholar
  22. 22.
    Willinger, W., Paxson, V., Taqqu, M.S.: Self-similarity and Heavy Tails: Structural Modelling of Network Traffic. In: Adler, R., Feldman, R., Taqqu, M.S. (eds.) A Practical Guide to Heavy Tails: Statistical Techniques and Applications, Birkhauser, Boston (1998)Google Scholar
  23. 23.
    Willinger, W., Taqqu, M.S., Sherman, R., Wilson, D.V.: Self-Similarity Through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level. IEEE/ACM Transactions on Networking 5(1), 71–86 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dimitrios P. Pezaros
    • 1
  • Manolis Sifalakis
    • 1
  • David Hutchison
    • 1
  1. 1.Computing Department, Infolab21, Lancaster University, Lancaster, LA1 4WAUK

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