Abstract
The paper examines self-similar 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 self-similar models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behaviour of IP network traffic processes are developed and applied. Simulations and numerical results are shown and analysed. Furthermore, simulations with stochastic and long range dependent traffic source models are conducted; and efficient algorithms for buffer overflow simulation in finite buffer single server model under self-similar traffic load are developed and applied. Simulations and numerical results are shown and analysed.
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