Statistical Modeling of Internet Traffic FlowLength and Flow Size
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Date
2019-12-20
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Addis Ababa University
Abstract
The growth of internet traffic forces telecom service providers to invest in new infrastructure
and/or expanding the network to achieve the desired Quality of Service (QoS) and cope
up the network congestion. But several techniques available for granting QoS and network
congestion. The first step is to understand the network performance. Network performance
needs accurate traffic modeling that has the potential to improve desired QoS, allocating
the network resources (i.e. bandwidth). This thesis presents a statistical model of the internet
traffic applications by their random nature of flow-length and flow-size. Wireshark
network monitoring and analysis tool is used to collect internet traffic data from the ethio
telecom core switch and generating experimental internet traffic data in controlled environment.
The experimental data are used to train and test the machine learning model that
helps to identify the internet applications. Firstly, identifying internet traffic applications
using machine learning classification techniques. Secondly, statistical methods are used to
fit the Cumulative Distribution Function (CDF) and select the parameters that best fitted in
both flow-length and flow-size for identified applications. Finally, deliver statistical model
to each applications and corresponding parameters. Recently internet traffic modeling is
applicable in capacity planning for traffic engineering, anomaly detection and performance
analysis are some of them. Based on the result found the Log-normal distribution is best
fitted to flow-length and flow size for three applications and Weibull distribution is for SSH
application in both flow length and flow size.
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Keywords
Internet Traffic Modeling, Identification, Flow Length, Flow Size, Distribution