This directory contains version 1.0 of an S program for synthesizing approximate fractional Gaussian noise. The program consists of 3 routines: ss.gen.fourier(n, H) Returns a sample path of n synthesized points with Hurst parameter H. n should be even. FGN.spectrum(lambda, H) Internal routine that returns an approximation to the power spectrum of fractional Gaussian noise at the given frequencies lambda and Hurst parameter H. FGN.B.est <- (lambda, H) Internal routine used in computing the approximation to the power spectrum. So an example of using it is just: sample.path <- ss.gen.fourier(8192, .8) Note that the mean and variance of the returned points is irrelevant, because any linear transformation applied to the points preserves their correlational structure (and hence their approximation to fractional Gaussian noise); and by applying a linear transformation you can transform the points to have any mean and variance you want. If you're using the sample paths for simulating network arrival counts, you'll want them to all be non-negative. Hopefully you have some notion of the mean and variance of your desired traffic, and can apply the corresponding transformation. If, after transforming, a fair number of the points are still negative, then perhaps your traffic is not well-modeled using fractional Gaussian noise. You might instead consider using an exponential transformation. All of this is discussed in the technical report: Fast Approximation of Self-Similar Network Traffic, Vern Paxson, technical report LBL-36750/UC-405, April 1995. URL:ftp://ftp.ee.lbl.gov/papers/fast-approx-selfsim.ps.Z The program is covered by a U.C. Regents copyright, spelled out in the file "COPYING". Questions/comments to vern@ee.lbl.gov.