On Generator of Network Arrivals with Self-Similar Nature

Recent empirical studies have shown that the modern computer network traffic is much more appropriately modelled by long range dependent self-similar processes than traditional short range dependent processes such as Poisson. Hence, if long range dependence is not considered for synthesizing experimental network traffic, it will lead to incorrect assessments of performance evaluation in network system. This arises the need of a well synthesizing trace with long range dependence. In this thesis, we present a filter-based method for synthesizing self-similar network traffic. This method improves the well-known methods of Paxison Fourier Transform and Random Midpoint Displacement in that the length of the synthesized traffic sequence does not need to pre-specify, and also the synthesized sequence is always non-negative. Although our method may have the drawback of becoming non-self-similar when the generated trace is aggregated under a very large window, this phenomenon turns out to match the measured behavior of true network traffic, where the self-similar nature only lasts beyond a practically manageable range, but disappears as the considered aggregated window is much further extended. i In memory of my father