Synthesis of fractional Gaussian noise using linear approximation for generating self-similar network traffic

The present paper focuses on self-similar network traffic generation. Netwo rk traffic modeling studies the generation of synthetic sequences. The gene rated sequences must have similar features to the measured traffic. Exact m ethods for generating self-similar sequences are not appropriate for long t races. Our main objective in the present paper is to improve the efficiency of Parson's method for synthesizing self-similar network traffic. Parson's method uses a fast, approximate synthesis for the power spectrum of the FG N and uses the inverse Fourier transform to obtain the time-domain sequence s. 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 p ower spectrum of the FGN. Our results show that long traces can be generate d in much less time. To compare our method with existing ones, we will meas ure the running time in generating long and short sample paths from the FGN . We will also conduct experiments to show that our method can generate sel fsimilar traffic for specified Hurst parameters with high accuracy.