ANALYSIS OF ETIU:RNET TRAFFIC

Abstract

The purpose of this work is to show ·the selfsimilarity
nature and long-range. dependence of
Ethernet nehvork traffic. Different mathematical
and graphicai techniques are used to show this
behavior. The result incfeed shows the long-range
dependence. or the presence of long memory in
Ethernet data traffic. A graphical proof of the selfsimilarity
nature of the traffic is shown, Also
Fractional. Auto';'Regressive Integrated Moving
Average (FARIMA) model is developed to capture
the long {lS 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,
Irandwidthallocation 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