Abstract
This paper presents a synthetic traffic modeling approach to generate bursty, long-range depen dent (LRD) traffic flows for ATM network simulations. The approach uses Fractional- ARIMA processes and a set of mathematical transformations to generate traffic streams with a wide range of user-specified traffic char acteristics. With this modeling approach, the user can control the short-range and long- range correlation structure in the generated traffic, as well as the marginal distribution. A description of the techniques is provided, along with a case study illustrating the use of these traffic generation techniques in a capacity planning study for ATM networks.
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