A Mixed-Fractal Model for Network Traffic
Li Li, Yudong Chen, Yi Zhang
TL;DR
This paper introduces a multi-fractal model for network traffic that explains crossover phenomena in Hurst exponent estimation, highlighting the impact of multiple components with different Hurst parameters on traffic behavior.
Contribution
The paper proposes a novel multi-fractal flow model that accounts for crossover phenomena in network traffic analysis, enhancing understanding and simulation accuracy.
Findings
Crossover phenomena occur when network flow has multiple components with different Hurst exponents.
The model explains how different Hurst components influence traffic behavior.
Potential usefulness in network traffic modeling and simulation.
Abstract
In this short paper, we propose a new multi-fractal flow model, aiming to provide a possible explanation for the crossover phenomena that appear in the estimation of Hurst exponent for network traffic. It is shown that crossover occurs if the network flow consists of several components with different Hurst components. Our results indicate that this model might be useful in network traffic modeling and simulation.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Theoretical and Computational Physics · Complex Systems and Time Series Analysis