Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

Radev, Dimitar; Lokshina, Izabella

doi:10.2478/v10187-010-0053-0

Abstract

The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

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Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

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