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Anja Feldmann's Publications

Dynamics of IP traffic: a study of the role of variability and the impact of control
Citation key FGHW-DIPT-99
Author Feldmann, Anja and Gilbert, Anna C. and Huang, Polly and Willinger, Walter
Pages 301–313
Year 1999
ISSN 0146-4833
DOI http://dx.doi.org/10.1145/316194.316235
Address New York, NY, USA
Journal ACM SIGCOMM Computer Communication Review (CCR)
Volume 29
Number 4
Publisher ACM Press
Abstract Using the ns-2-simulator to experiment with different aspects of user- or session-behaviors and network configurations, 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 multifractal and suggest novel ways for identifying, for example, performance bottlenecks or non-conforming connections. This paper focuses on the qualitative aspects of a wavelet-based scaling analysis and de-emphasizes the quantitative context 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.
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