Characterizing And Modeling Wan Egress Traffic

Computer networks exhibit complex characteristics due to the heterogenous nature of trafficrnrunning through the network. This makes the design of reliable networks and network servicesrndifficult. To have a design of robust and reliable networks, a detailed understanding of trafficrncharacteristics of the network is needed which will lead to distinguish the traffic model it fits. rnIn this paper, it is showed that the WAN egress traffic possess self-similar characteristics, usingrndifferent mathematical techniques. And also, the presence of long memory in WAN egress trafficrnis shown by the Autocorrelation Function of the trace. Additionally, it is showed that one of thernself-similar long memory models, Fractional Auto-Regressive Integrated Moving Averagern(FARIMA) model, best capture the collected WAN traffic data. To model the traffic data firstrnstationarity was tested using Augmented Dickey Fuller (ADF) test. The AR and MA terms of thernmodel are estimated using the ACF and PACF plot. To test the model, Autocorrelation function isrnused, and it is found that the Autocorrelation function of the approximated data has a resemblancernto the Autocorrelation function of the collected data.

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