Statistical Modeling Of Internet Trafc Flowlength And Flow Size

The growth of internet traffic forces telecom service providers to invest in new infrastructurernand/or expanding the network to achieve the desired Quality of Service (QoS) and copernup the network congestion. But several techniques available for granting QoS and networkrncongestion. The first step is to understand the network performance. Network performancernneeds accurate traffic modeling that has the potential to improve desired QoS, allocatingrnthe network resources (i.e. bandwidth). This thesis presents a statistical model of the internetrntraffic applications by their random nature of flow-length and flow-size. Wiresharkrnnetwork monitoring and analysis tool is used to collect internet traffic data from the ethiorntelecom core switch and generating experimental internet traffic data in controlled environment.rnThe experimental data are used to train and test the machine learning model thatrnhelps to identify the internet applications. Firstly, identifying internet traffic applicationsrnusing machine learning classification techniques. Secondly, statistical methods are used tornfit the Cumulative Distribution Function (CDF) and select the parameters that best fitted inrnboth flow-length and flow-size for identified applications. Finally, deliver statistical modelrnto each applications and corresponding parameters. Recently internet traffic modeling isrnapplicable in capacity planning for traffic engineering, anomaly detection and performancernanalysis are some of them. Based on the result found the Log-normal distribution is bestrnfitted to flow-length and flow size for three applications and Weibull distribution is for SSHrnapplication in both flow length and flow size.

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