New Models for Pseudo Self-Similar Traffic

Abstract After measurements on a LAN at Bellcore, it is known that data traffic is extremely variable on timescales ranging from milliseconds to days. The traffic behaves quite different from what has been assumed until now; traffic sources were generally characterized by short-term dependences but characteristics of the measured traffic have shown that it is long-term dependent. Therefore, new models (such as fractional Brownian motion, ARIMA processes and chaotic maps) have been applied. Although they are not easily tractable, one big advantage of these models is that they give a good description of the traffic using few parameters. In this paper, we describe a Markov chain emulating self-similarity which is quite easy to manipulate and depends only on two parameters (plus the number of states in the Markov chain). An advantage of using it is that it is possible to re-use the well-known analytical queuing theory techniques developed in the past in order to evaluate network performance. The tests performed on the model are the following: Hurst parameter (by the variances method) and the so-called “visual” test. A method of fitting the model to measured data is also given. In addition, considerations about pseudo long-range dependences are exposed.