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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3799

Authors: Yalemzewd, Negash
Advisors: Prof. Devarajan
Copyright: Jun-2001
Date Added: 19-Nov-2012
Abstract: A common assumption in modeling computer networks is that packet arrivals occur as a Poisson process. However, data communication traffic levels fluctuate over time, and delays through congestion can occur even on lightly utilized links. These fluctuations can occur over very short periods of time giving rise to the concept of a burst of traffic. Bursts of traffic can be of intensity more than five times the average utilization so that if a user is trying to send data and it coincides with a burst the user will experience delays. Traffic that exhibits these wild fluctuations is known as "bursty" traffic. Understanding the nature of traffic in high-speed, high-bandwidth communications systems such as B-ISDN is essential for engineering, operations, and performance evaluation of these networks. In a first step towards this goal, it is important to know the traffic behavior of some of the expected major contributors to future high-speed network traffic. In this research work traffic from an Ethernet LAN is studied for it behavior. The purpose of this work is to show the self-similarity nature and long-range dependence of Ethernet network traffic. Different mathematical and graphical 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 calculated from the data. The model could be used in different network application like congestion control in highbandwidth networks, bandwidth allocation and the like. All the results in this work are supported by a rigorous statistical analysis of the collected data coupled with a discussion of the underlying mathematical and statistical properties of long memory processes.
URI: http://hdl.handle.net/123456789/3799
Appears in:Thesis - Electrical Engineering

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