Fractal Analysis On Internet Traffic Time Series

TL;DR

This paper demonstrates that internet traffic time series exhibit fractal properties and long-range dependence, using various fractal analysis methods to characterize local irregularities and self-similarity.

Contribution

The study applies multiple fractal analysis techniques to internet traffic data, confirming self-similarity and long-range dependence in the traffic patterns.

Findings

Internet traffic exhibits self-similarity.

Time-scale analysis effectively characterizes local irregularity.

Traffic shows fractal characteristics with long-range dependence.

Abstract

Fractal behavior and long-range dependence have been observed in tele-traffic measurement and characterization. In this paper we show results of application of the fractal analysis to internet traffic via various methods. Our result demonstrate that the internet traffic exhibits self-similarity. Time-scale analysis show to be an effective way to characterize the local irregularity. Based on the result of this study, these two Internet time series exhibit fractal characteristic with long-range dependence.