On the Use of Self-Similar Processes in Network Simulation

Several recent traffic measurement studies have convincingly shown the presence of self-similarity in modern high-speed networks, involving a very important revolution in the stochastic modeling of traffic. Thus the use of self-similar processes has opened new problems and research fields in network performance analysis, mainly in simulation studies, where the efficient synthetic generation of sample paths (traces) corresponding to self-similar traffic is one of the main topics. In this article, we justify the selection of interarrival time instead of counting processes for modeling arrivals. Also, we discuss the advantages and drawbacks of the most important self-similar processes when applied to traffic modeling in simulation studies, proposing the use of models based in F-ARIMA, mainly due to their flexibility to capture both long- and short-range correlations. However, F-ARIMA processes have been little used in simulation studies, mainly because the synthetic generation methods available in the literature are very inefficient compared with those for FGN. In order to solve this problem, we propose a new method that can generate high-quality traces corresponding to a F-ARIMA(p, d, q) process. A comparison with existing methods shows that the new method is significantly more efficient, and even slightly better than the best method for FGN