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Self-Similar Ethernet LAN Traffic. Carey Williamson. University of Calgary. Introduction. The original paper on network traffic self-similarity appeared at the 1993 ACM SIGCOMM Conference on Communications Architectures and Protocols
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Self-Similar Ethernet LAN Traffic Carey Williamson University of Calgary
Introduction • The original paper on network traffic self-similarity appeared at the 1993 ACM SIGCOMM Conference on Communications Architectures and Protocols • Authors: Will Leland, Murad Taqqu, Walter Willinger, and Daniel Wilson (Leland et al. 1993)
Introduction (Cont’d) • Extended version of the paper appeared in the IEEE/ACM Transactions on Networking, Vol. 2, No. 1, February 1994 • One of the landmark papers of the ‘90’s • Highly regarded, influential, one of the most cited papers in the last 3 years
Main Contributions • Identified presence of self-similarity property in aggregate Ethernet traffic • Defined methodology for testing for the presence of self-similarity • variance-time plot • R/S statistic • periodogram (power spectrum) • Proposed explanations/models for SS
Self-Similarity: A Hot Topic • Several papers since then have identified network traffic self-similarity in OTHER types of traffic (video, Internet, Web) • Several models for self-similar traffic have been proposed in the literature • Several studies of the performance implications of self-similar traffic and long-range dependence
Measurement Study • Detailed measurement study of very lengthy Ethernet packet traces, with high resolution timer, and lots of storage space • One of the traces presented in their paper is a 27.5 hour trace • Over 20 million packets
Data Analysis • Detailed statistical analysis: • aggregation, autocorrelation, R/S analysis, variance-time plot, periodograms, Whittle’s estimator, maximum likelihood ... • Very rigourous: confidence intervals, sophisticated statistical tests, sound methodology, ... • A wonderful paper to read (over and over)
Main Results • Aggregate Ethernet LAN traffic is self-similar • Burstiness across many time scales • Hurst parameter 0.7 < H < 0.9 • H is larger when network utilization is higher (e.g., 0.9 when U = 15%) • Self-similarity present on all LAN’s tested
Conclusions • Self-similarity is present in aggregate Ethernet LAN traffic • Traffic does not aggregate well at all • Law of large numbers may not hold! • Poisson models (or Markovian models of any sort) do not capture reality at all • Important to consider self-similar traffic