Download Multifractal Based Network Traffic Modeling by Krishna P. Murali, Vikram M. Gadre, Uday B. Desai (auth.) PDF

By Krishna P. Murali, Vikram M. Gadre, Uday B. Desai (auth.)

Multifractal dependent community site visitors Modeling presents an summary of latest broadband site visitors modeling according to the Poisson procedure and its variations just like the MM1 types. It additionally presents excellent insurance of versions in accordance with self-similar methods. through the ebook, the authors have inquisitive about the matter of broadband site visitors modeling holding in brain lengthy diversity dependencies in broadband site visitors.

Graduate scholars, researchers, and contributors new to the parts of teletraffic modeling and conversation community engineering will locate this paintings in particular valuable. The publication may be used as a textbook for a graduate point direction on Teletraffic Modeling.

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42) Let 7 = 1, the following can be arrived at from the above equation. 45) C can be substituted by another positive constant if. 48) Long Range Dependence Property Let us define a new process Y(t) = X(t) - X(t -1) which is the increment process corresponding to X(t) (sampled at every integer instant). The long range dependence characteristics of the increment process can be obtained from the analysis of the covariance of the process Y(t). 51) 2 The asymptotic behavior of p(k) can be obtained by using Taylor series expan0'2 sion.

Multifractal Based Network Traffic Modeling © Kluwer Academic Publishers 2003 16 1. MULTIFRACTAL BASED NEIWORK TRAFFIC MODELING Random Processes A random process or a stochastic process is a sequence of random variables {X(t), t E T} indexed by the parameter t which takes values in the set T. If T is a discrete set, then the process is discrete time in nature and if the set T is continuous, the process is like wise continuous in nature. 1. 1. Random Process - illustration Random processes are usually specified by the associated probability density as well as the first and second order moments.

M model, and the original data along with other models mentioned in the literature. We also investigate the nature of the inter-departure process from a queue when the input inter arrival process has multifractal characteristics. We investigate the problem of multiplexing of traffic that exhibits multifractal nature in Chapter 6. The multifractal spectrum of a WAN traffic trace is compared with multiplexed LAN traces with varying degrees of multiplexing. The variation of the entropy is analyzed and compared with experimental results obtained by using the inter arrival time data.

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