Analysis of ethernet traffic
The purpose of this work is to show the self-similarity nature and long-range. dependence of Ethernet network traffic. Different mathematical and graphicai techniques are used to show this behavior. The result indeed shows the long-range dependence or the presence of long memory in Ethernet data traffic. A graphical proof of the self similarity nature of the traffic is shown, Also Fractional Auto Regressive Integrated Moving Average (FARIMA) model is developed to capture the long as well as the short memory properties of the collected Ethernet traffic data. The model is found to be in good agreement with the periodogram calculatedfrom the data. The model could be used in different network application like congestion control in high-bandwidth networks, bandwidth allocation and the like. All the results in this work are supported by a rigorous statistical anafysis of the collected data coupled with a discussion of the underlying mathematical' and statistical properties of long memoryprocesses.