A Generator of Pseudo-Random Self-Similar Sequences Based on SRA
Degree GrantorUniversity of Canterbury
It is generally accepted that self-similar (or fractal) processes may provide better models for teletraffic in modern computer networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of computer networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A generator of pseudo-random self-similar sequences, based on the SRA method , is implemented and analysed in this report. Properties of this generator were experimentally studied in the sense of its statistical accuracy and the time required to produce sequences of a given (long) length. This generator shows acceptable level of accuracy of the output data (in the sense of relative accuracy of the Hurst parameter) and is fast. The theoretical algorithmic complexity is O(n) .