Accurate and fast replication on the generation of fractal network traffic using alternative probability models

Synthetic self-similar traffic in computer networks simulation is of imperative significance for the capturing and reproducing of actual Internet data traffic behavior. A universally used procedure for generating self-similar traffic is achieved by aggregating On/Off sources where the active (On) and idle (Off) periods exhibit heavy tailed distributions. This work analyzes the balance between accuracy and computational efficiency in generating self-similar traffic and presents important results that can be useful to parameterize existing heavy tailed distributions such as Pareto, Weibull and Lognormal in a simulation analysis. Our results were obtained through the simulation of various scenarios and were evaluated by estimating the Hurst (H) parameter, which measures the self-similarity level, using several methods.