Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini

This project is to generate the self-similar network traffic. It is generally accepted
that self-similar or fractal process may provide better models for in modern network
traffic than Poisson process. Poisson arrival processes are not self-similar, regardless
of degree of aggregation. The way to solve this problem, we applied the existed
method in visual C++ programming with used the Random midpoint Displacement
(RMD) algorithm. That program we need the sequence of the random number as a
data. The data was generated depends on the power of two of data. The numbers of
data will be analyzed using the R/S Statistic program and Variance Time Plot
program. That analysis programs were running in MathCAD v12 platform. The graft
will be display after the data is running in the analysis programs as result. The new
values of Hurst will be appear as a results whether the self-similar or not. After the
analysis process, the result from the R/S Statistic and Variance Tome Plot were not
accurate. The new value of Hurst was not exactly same with the expected value of
Hurst. As a conclusion, using RMD algorithm the result are more satisfy compare
using the traditional process because the result are more accurate are more faster.
The RMD fastest in term of computational time but do not accurately reflect the
Hurst parameter.