The paper examines self-similar properties of real telecommunications 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. The algorithms for modeling the sequentional generators and the fixed-length sequence generators to simulate self-similar behavior of real teletraffic data are developed and applied. The algorithms to simulate the buffer overflow in finite buffer single server model under self-similar traffic load are also developed and applied. Numerical examples and simulation results are provided.
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