By Krishna P. Murali, Vikram M. Gadre, Uday B. Desai (auth.)
ISBN-10: 1461351073
ISBN-13: 9781461351078
ISBN-10: 1461504996
ISBN-13: 9781461504993
Multifractal dependent community site visitors Modeling offers an summary of present broadband site visitors modeling in keeping with the Poisson technique and its versions just like the MM1 types. It additionally presents excellent insurance of types in line with self-similar approaches. through the e-book, the authors have interested in the matter of broadband site visitors modeling protecting in brain lengthy variety dependencies in broadband site visitors.
Graduate scholars, researchers, and participants new to the parts of teletraffic modeling and communique community engineering will locate this paintings specially worthy. The booklet may be used as a textbook for a graduate point direction on Teletraffic Modeling.
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Extra resources for Multifractal Based Network Traffic Modeling
Example text
The experiments conducted with a multi- dimensional Kalman filter also give promising results where the burstiness from various different traces are predicted simultaneously. • The question of complexity of broadband network traffic is also addressed in this monograph. The study was conducted for the inter arrival time data, as well as the VBR video traces. We define the volatility parameter for the traffic data and analyze the predictability of this parameter. In particular, we look at how the entropy of the volatility parameter changes with time.
M multiplicative multifractal model provides a simple and efficient technique to synthesize VBR traces. The statistical tests include comparison of the higher order moments of the aggregated traffic, index of dispersion of counts and multifractal spectrum f (a). For performance analysis, we conduct queuing experiments on ATM traffic obtained from the original and simulated traces. We have compared the queue length distribution of the aggregated trace traffic, the variation of cell delay and loss with the load.
15. Heavy tailed Process the Hill estimator [66]. The Hill estimator gives an estimate of, as a function of the k largest element in the data set. 103) where X(l) ~ ... ~ X(n) denote the data set's order statistics. The Hill estimator is plotted against increasing values of k. The Q-Q plots (quantilequantile) plots can also be used to investigate whether the data shows heavy tailed characteristics. Heavy tailedness leads to scaling properties in the data. The method of estimating whether a distribution is heavy tailed or not can be done by analyzing the scaling property of the data [67].
Multifractal Based Network Traffic Modeling by Krishna P. Murali, Vikram M. Gadre, Uday B. Desai (auth.)
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