Generation of Synthetic Network Traffic Series Using a Transformed Autoregressive Model Based Adaptive Algorithm

Abstract

In this paper, we propose an adaptive algorithm to estimate the parameters of the Transformed Autoregressive Moving Average (TARMA) model in order to capture the autocorrelation function and the cumulative density function of the desired network traffic trace. We compare the performance of the proposed on-line modeling approach to those of the Autoregressive Moving Average Model (ARMA) and of the on-batch Transformed Model in terms of mean, variance, moments, autorrelations and probability density function. A transmission link composed of a single server with buffer is also simulated, which proves the efficiency of the proposed model in describing real traffic traces. The simulations carried out in this work show that the adaptive TARMA model outperforms in general the other considered autoregressive models.