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Eirini Spartinou's Publications

On TCP and self-similar traffic
Citation key FLFMTW-TCPSST-05
Author Figueiredo, Daniel R. and Liu, Benyuan and Feldmann, Anja and Misra, Vishal and Towsley, Don and Willinger, Walter
Pages 129–141
Year 2005
ISSN 0166-5316
DOI http://dx.doi.org/10.1016/j.peva.2004.11.004
Address Amsterdam, The Netherlands, The Netherlands
Journal Performance Evaluation, special issue on Long range dependence and heavy tail distributions
Volume 61
Number 2-3
Publisher Elsevier Science Publishers B. V.
Abstract We re-examine the same TCP trace that was used by Veres et al. [A. Veres, M. Boda, The chaotic nature of TCP congestion control, in: Proceedings of the IEEE INFOCOM, 2000] to claim that TCP creates self-similar traffic. A careful reassessment of their data analysis shows that this claim is not justified and suggests that the TCP trace in question is not consistent with (asymptotic second-order) self-similarity or long-range dependence (LRD). We illustrate the reasons that led to the claim in [A. Veres, M. Boda, The chaotic nature of TCP congestion control, in: Proceedings of the IEEE INFOCOM, 2000] and provide some practical guidelines for assessing a statistical characteristic of trace data such as LRD that is defined in strictly asymptotic terms. Our conclusion is in full agreement with the findings obtained from analyzing a much longer TCP trace (resulting from repeating the same simulation as in [A. Veres, M. Boda, The chaotic nature of TCP congestion control, in: Proceedings of the IEEE INFOCOM, 2000], but running it for a longer period) and with analytical results derived from a detailed Markovian model of TCP. These results show that the traffic generated by a long-lived TCP connection, while exhibiting pronounced correlations over a predictable finite range of time-scales, cannot be (asymptotically second-order) self-similar or exhibit LRD. Our work serves as a reminder of the importance of careful trace analysis and detailed examination (and cross-validation) of alternative explanations when establishing or characterizing the generality of any particular finding about Internet traffic.
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