Self-similarity through high-variability

作者：Walter Willinger, Murad S. Taqqu, Robert P. Sherman, Daniel V. Wilson · 发表于：ACM SIGCOMM Computer Communication Review · 年份：1995 · DOI：10.1145/217391.217418 · 被引用次数：143 · 研究领域：Network Traffic and Congestion Control、Complex Network Analysis Techniques、Nonlinear Dynamics and Pattern Formation

A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) - in sharp contrast to commonly made traffic modeling assumptions. In this paper, we provide a plausible physical explanation for the occurrence of self-similarity in high-speed network traffic. Our explanation is based on convergence results for processes that exhibit high variability (i.e., infinite variance) and is supported by detailed statistical analyses of real-time traffic measurements from Ethernet LAN's at the level of individual sources.Our key mathematical result states that the superposition of many ON/OFF sources (also known as packet trains ) whose ON-periods and OFF-periods exhibit the Noah Effect (i.e., have high variability or infinite variance) produces aggregate network traffic that features the Joseph Effect (i.e., is self-similar or long-range dependent). There is, moreover, a simple relation between the parameters describing the intensities of the Noah Effect (high variability) and the Joseph Effect (self-similarity). An extensive statistical analysis of two sets of high time-resolution traffic measurements from two Ethernet LAN's (involving a few hundred active source-destination pairs) confirms that the data at the level of individual sources or source-destination pairs are consistent with the No...