A fast method for generating self-similar network traffic

Recently, self-similar/fractal traffic models have been shown to be applicable to a variety of network traffic. This gives rise to new and challenging problems for statistical inference, stochastic modeling and synthetic traffic generation. The present paper focuses on self-similar traffic generation. Network traffic modeling studies the generation of synthetic sequences. The generated sequences must have similar features to the measured traffic. Exact methods for generating self-similar sequences from the fractional Gaussian noise (FGN) and the fractional autoregressive integrated moving average process models are not appropriate for long traces. Our main objective in the present paper is to improve the efficiency of the method presented by Paxson [20] for synthesizing self-similar sample paths. Parson's method uses a fast, approximate synthesis for the power spectrum of the FGN and uses the inverse Fourier transform to obtain the time-domain sequences. We demonstrate that a linear approximation can be used to determine the power spectrum of the FGN. This linear approximation reduces the complexity of the computation without compromising the accuracy in synthesizing the power spectrum of the FGN. Our results show that long traces can be generated in much less time. To compare our method with existing ones, we will measure the running time in generating long and short sample paths from the FGN. Fire will also conduct experiments to show that our method can generate self-similar traffic for specified Hurst parameters with high accuracy.