Harun, Hani Hamira (2006) Selfsimilar network traffic using Successive Random Addition (SRA) algorithm / Hani Hamira Harun. Degree thesis, Universiti Teknologi MARA.

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Abstract
Selfsimilar traffic has an underlying dependence structure which exhibits longrange dependence. This is in contrast to classical traffic models, such as Poisson, which exhibit shortrange dependence. Selfsimilar traffic may also exhibit shortrange dependence, but this is on its own insufficient to accurately parametric the traffic. Studying selfsimilar traffic requires models for analytical work and generators for simulation. Having generating algorithms that close to reflect real traffic is important as they allow us to perform simulations that are similar to the real network traffic. Without this, the results from simulations would not accurately reflect the results that would be expected in the real world. In this project, we have used the Successive random algorithm (SRA). Then, we have decided to use Variance time plot and R/S statistics as our statistical analysis tools. We have test the sample path between 0.5<H<1. After we test on the SRA algorithm, we found that the results are not accurate. But compares to RMD, the SRA samples result be more accurate. In term data generation, SRA be slower than dFGN. COPYRIGHT
Item Type:  Thesis (Degree)  

Creators: 


Subjects:  Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science 

Divisions:  Faculty of Computer and Mathematical Sciences  
Item ID:  850  
Last Modified:  30 Oct 2018 02:25  
Depositing User:  Staf Pendigitalan 1  
URI:  http://ir.uitm.edu.my/id/eprint/850 
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