An empirical comparison of generators for self similar simulated traffic

It is generally recognised that aggregated network traffic is self similar and that self similar traffic models should be used in simulation experiments when assessing the performance of a network. Many generators have been proposed to synthetically produce self similar simulation input; however most of them require the trace length to be known a priori. Four generators that allow continuous generation of self similar time series are evaluated in this work with respect to their ability to reproduce the desired level of self similarity. This extensive investigation uses ten times as many traces and twice the number of parameter values as previously reported. Three of the tested generators perform well but surprisingly the generator supplied with a widely used commercial network simulator is unusable. The reported results indicate that the generator based on multiplexing strictly alternating ON/OFF sources may perform better than generators based on chaotic maps, provided that more than 100 ON/OFF sources can be used. Three estimators for the degree of self similarity of a time series have been evaluated as part of the process, and the only acceptable one is based on a Wavelet decomposition of the traffic trace.