Fast, Approximate Synthesis of Fractional Gaussian Noise for Generating Self-Similar Network Traffic

TL;DR

This paper introduces a fast Fourier transform-based method for efficiently synthesizing approximate fractional Gaussian noise, enabling realistic self-similar network traffic simulation with improved speed and accuracy over existing techniques.

Contribution

The paper presents a novel FFT-based approach for rapid synthesis of fractional Gaussian noise, improving efficiency and approximation quality for self-similar traffic modeling.

Findings

Method is as fast or faster than existing techniques.

Generated sample paths closely approximate true self-similar processes.

Speeds up evaluation of Hurst parameter using Whittle's estimator.

Abstract

Recent network traffic studies argue that network arrival processes are much more faithfully modeled using statistically self-similar processes instead of traditional Poisson processes [LTWW94,PF95]. One difficulty in dealing with self-similar models is how to efficiently synthesize traces (sample paths) corresponding to self-similar traffic. We present a fast Fourier transform method for synthesizing approximate self-similar sample paths for one type of self-similar process, Fractional Gaussian Noise, and assess its performance and validity. We find that the method is as fast or faster than existing methods and appears to generate close approximations to true self-similar sample paths. We also discuss issues in using such synthesized sample paths for simulating network traffic, and how an approximation used by our method can dramatically speed up evaluation of Whittle's estimator for H, the Hurst parameter giving the strength of long-range dependence present in a self-similar time series.