Estimating the parameters of measured self similar traffic for modeling in OPNET

Over the last ten years, new models of network traffic in the Internet environment have been developed, which are different to traditional models such are Poisson and Markov. This paper describes the estimating of measured self similar traffic´s parameters for modeling in OPNET. Network traffic was captured using a sniffer. We estimated the Hurst parameter (H) for the arrival process, and the fitted distributions for the measured data (packet size and inter-arrival processes). Using the autocorrelation function of the process, we determined long-range or short-range dependence. Finally, we modeled the measured test signal in OPNET using raw packet generator (RPG), and IP stations.