Network traffic has fractal properties such as impulsiveness, selfsimilarity,
and long-range dependence over several time scales, from
milliseconds to minutes. These features have motivated the development of new
traffic models and traffic control algorithms. This book presents a new statespace
model for Internet traffic, which is based on a finite-dimensional
representation of the Autoregressive Fractionally Integrated Moving Average
(ARFIMA) random process. The modeling via Autoregressive (AR) processes is
also investigated.
Content: Introduction, The Fractal Nature of Network Traffic, Modeling of Long-
Range Dependent Teletraffic, State-Space Modeling, Modeling of Internet Traffic
forecast, long memory, long-range dependence, network traffic,
prediction, self-similar