We seek to unearth which distribution best describes traffic
behaviour to help solve the problem of congestion on campus from a mathematical
point of view. Telecommunications traffic engineering (i.e. Tele -traffic
engineering) is the application of traffic engineering theory to
telecommunications. Tele-traffic engineers use their cognition of statistics
including queuing theory, the nature of traffic, their practical fashion model,
and their mensuration and computer simulation to make predictions and to
program telecommunication networks such as a telephone network or the Internet.
Internet traffic is a flow of data across the internet. This flow exhibits
certain behaviour in accordance with a Probability distribution. Statistical analysis was performed to
understand the characteristics of the traffic population. The empirical
cumulative distribution process (CDF) together with essential statistical
parameters were benchmarked. The goodness of Fit (GOF) test using the
Anderson-Darling (AD) estimation method was applied to establish the best
probability distribution model which describes the situation alongside with
Probability plot. Deep-down opinion or analysis hints that if the period of
time (or space) of grouping is increased well enough, the degree of data
relationships will eventually become unimportant by scaling. The N-Flow traffic
model was used to determine the burstiness of internet traffic in finite
sessions. The Empirical and Theoretical CDF graph was used to determine the
discrepancies between them. In the end, it was discovered that the data best
fit normal distribution. From experimental and theoretical analysis, it is
clear that the internet traffic behaviour in Ghana Technology University
College is normally distributed.
Author(s)details:-
Emmanuel Kwame Mensah
Department of Computer and Telecommunication Engineering, Faculty of
Engineering, Ghana Communication Technology, PMB 100 Accra-North, Ghana.
Please See the book
here :- https://doi.org/10.9734/bpi/rumcs/v7/2039G
No comments:
Post a Comment