Dynamics of IP traffic: a study of the role of variability and the impact of control

  • Authors:

  • Anja Feldmann;Anna C. Gilbert;Polly Huang;Walter Willinger

  • Affiliations:

  • AT&T Labs-Research, Florham Park, NJ;AT&T Labs-Research, Florham Park, NJ;USC/ISI, Los Angeles, CA;AT&T Labs-Research, Florham Park, NJ

  • Venue:
  • Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
  • Year:
  • 1999

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Abstract

Using the ns-2-simulator to experiment with different aspects of user- or session-behaviors and network configurations and focusing on the qualitative aspects of a wavelet-based scaling analysis, we present a systematic investigation into how and why variability and feedback-control contribute to the intriguing scaling properties observed in actual Internet traces (as our benchmark data, we use measured Internet traffic from an ISP). We illustrate how variability of both user aspects and network environments (i) causes self-similar scaling behavior over large time scales, (ii) determines a more or less pronounced change in scaling behavior around a specific time scale, and (iii) sets the stage for the emergence of surprisingly rich scaling dynamics over small time scales; i.e., multifractal scaling. Moreover, our scaling analyses indicate whether or not open-loop controls such as UDP or closed-loop controls such as TCP impact the local or small-scale behavior of the traffic and how they contribute to the observed multifractal nature of measured Internet traffic. In fact, our findings suggest an initial physical explanation for why measured Internet traffic over small time scales is highly complex and suggest novel ways for detecting and identifying, for example, performance bottlenecks.This paper focuses on the qualitative aspects of a wavelet-based scaling analysis rather than on the quantitative use for which it was originally designed. We demonstrate how the presented techniques can be used for analyzing a wide range of different kinds of network-related measurements in ways that were not previously feasible. We show that scaling analysis has the ability to extract relevant information about the time-scale dynamics of Internet traffic, thereby, we hope, making these techniques available to a larger segment of the networking research community.