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Abstrac(We demonstrate that Ethernet LAN traffic is statistically se~-simi/ar, that none of the commonly used traffic models is able to capture this fra([al-like behavior, that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks, and that aggregating streams of such traffic typically intensifies(More)
A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is <i>self-similar</i> or <i>long-range dependent</i> 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(More)
We demonstrate that Ethernet local area network (LAN) traffic is statistically <i>self-similar,</i> that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks. Intuitively, the critical characteristic of this(More)
We state and prove the following key mathematical result in self-similar traffic modeling: the superposition of many <i>ON/OFF</i> sources (also known as <i>packet trains</i>) with strictly alternating <i>ON</i>- and <i>OFF</i>-periods and whose <i>ON</i>-periods or <i>OFF</i>-periods exhibit the <i>Noah Effect</i> (i.e., have high variability or infinite(More)
In recent years, methods to estimate the memory parameter using wavelet analysis have gained popularity in many areas of science. Despite its widespread use, a rigorous semi-parametric asymptotic theory, comparable to the one developed for Fourier methods, is still missing. In this contribution, we adapt to the wavelet setting the classical semi-parametric(More)