A filter-based self-similar trace synthesizer

Recent empirical studies have shown that 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. Thus, if its self-similar nature is not considered in the synthesis of experimental network data, incorrect performance assessments for network systems may result. This raises the need of a self-similar trace synthesizing algorithm with long-range dependence. In this paper, we propose and examine the feasibility of a filter-based method for the synthesis of self-similar network traces. The proposed approach can alleviate the problems encountered by conventional synthesizers, such as random midpoint displacement and Paxson's spectrum fitting, which cannot generate self-similar traces on the fly and may give negative numbers. Additionally, the extended range of self-similarity of the filtered approach can be easily managed by the filter truncation window; therefore, a trace that faithfully matches the measured behavior of true network traffic, where the self-similar nature only lasts beyond a certain range but disappears as the considered aggregated window is much further extended, can be generated.