The Modelling of Ethernet Data and of Signals that are Heavy‐tailed with Infinite Variance

We provide an overview of some of the research of the last ten years involving computer network data traffic. We describe the original Ethernet data study which suggested that computer traffic is inherently different from telephone traffic and that in the context of computer networks, self‐similar models such as fractional Brownian motion, should be used. We show that the on–off model can physically explain the presence of self‐similarity. While the on–off model involves bounded signals, it is also possible to consider arbitrary unbounded finite‐variance signals or even infinite‐variance signals whose distributions have heavy tails. We show that, in the latter case, one can still obtain self‐similar processes with dependent increments, but these are not the infinite‐variance fractional stable Lévy motions which have been commonly considered in the literature. The adequate model, in fact, can either have dependent or independent increments, and this depends on the respective size of two parameters, namely, the number of workstations in the network and the time scale under consideration. We indicate what happens when these two parameters become jointly asymptotically large. We conclude with some comments about high frequency behaviour and multifractals.