Temporal Patterns and Properties in Multiple-Flow Interactions

  • Marat Zhanikeev
  • Yoshiaki Tanaka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4238)


It is widely recognized that today’s Internet traffic is mostly carried by a relatively small number of elephant flows while mice flows constitute up to 80% of all active flows at any given moment in time. Although there are many research works that perform structural analysis of flows based on their size, rate, and lifespan, such analysis says very little about temporal properties of interactions among multiple flows originating from different applications. This paper focuses on temporal analysis of flows in attempt to grasp properties and patterns of flows that are related to application and user behaviour and can be captured only in the temporal view of traffic.


Heavy Tail Localize Match Match Ratio Rate List Source Interactivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Claffy, K., Braun, H., Polyzos, G.C.: A parameterizable methodology for internet traffic flow profiling. IEEE Journal on Selected Areas in Communications 13(8), 1481–1494 (1995)CrossRefGoogle Scholar
  2. 2.
    Abrahamsson, H., Ahlgren, B.: Temporal characteristics of large IP traffic flows. Technical Report T2003.27, Swedish Institute of Computer Science (2004)Google Scholar
  3. 3.
    Papagiannaki, K., Taft, N., Bhattacharyya, S., Thiran, P., Salamatian, K., Diot, C.: A pragmatic definition of elephants in internet backbone traffic. In: 2nd ACM SIGCOMM Workshop on Internet Measurement, pp. 175–176 (2002)Google Scholar
  4. 4.
    Papagiannaki, K., Taft, N., Bhattacharyya, S., Thiran, P., Salamatian, K., Diot, C.: On the Feasibility of Identifying Elephants in Internet Backbone Traffic. Tech.Report no. RR01-ATL-110918, Sprint ATL (2001)Google Scholar
  5. 5.
    Papagiannaki, K., Taft, N., Diot, C.: Impact of Flow Dynamics on Traffic Engineering Design Principles. IEEE INFOCOM 4, 2295–2306 (2004)CrossRefGoogle Scholar
  6. 6.
    Crovella, M.: Performance Evaluation with Heavy Tailed Distributions. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 2001. LNCS, vol. 2221, pp. 1–11. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Willinger, W., Taqqu, M., Sherman, R., Wilson, D.: 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
  8. 8.
    Crovella, M., Taqqu, M.: Estimating the Heavy Tail Index From Scaling Properties. Methodology and Computing in Applied Probability 1, 55–79 (1999)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Erramilli, A., Pruthi, P., Willinger, W.: Self-Similarity in High-Speed Network Traffic Measurements: Fact or Artifact? In: VTT Symposium, vol. 154, pp. 299–310 (1995)Google Scholar
  10. 10.
    Zhang, Y., Breslau, L., Paxson, V., Shenker, S.: On the characteristics and origins of internet flow rates. In: ACM SIGCOMM, pp. 309–322 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marat Zhanikeev
    • 1
  • Yoshiaki Tanaka
    • 1
    • 2
  1. 1.Global Information and Telecommunication InstituteWaseda UniversityTokyoJapan
  2. 2.Advanced Research Institute for Science and EngineeringWaseda UniversityTokyoJapan

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