ADVANCED MODELS AND ALGORITHMS FOR SELF-SIMILAR IP NETWORK TRAFFIC SIMULATION AND PERFORMANCE ANALYSIS

Dimitar Radev – Izabella Lokshina

   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.

Keywords: communication networks, IP network traffic, long-range dependent self-similar processes, advanced generators of self-similar teletraffic

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