Multifractal Based Network Traffic Modeling provides an overview of existing broadband traffic modeling based on the Poisson process and its variants like the MM1 models. It also provides very good coverage of models based on self-similar processes. Throughout the book, the authors have focused on the problem of broadband traffic modeling keeping in mind long range dependencies in broadband traffic.
Graduate students, researchers, and individuals new to the areas of teletraffic modeling and communication network engineering will find this work especially helpful. The book could also be used as a textbook for a graduate level course on Teletraffic Modeling.
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Hardcover. Condition: new. Hardcover. This text provides an overview of existing broadband traffic modelling based on the Poisson process and its variants like the MM1 models. It also provides coverage of models based on self-similar processes. Throughout the book, the authors have focused on the problem of broadband traffic modelling keeping in mind long range dependencies in broadband traffic. A new model is being proposed for modeling broadband traffic based on the theory of multiplicative cascade processes. This is referred to as the Variance Gaussian Multiplier (V.V.G.M) multiplicative cascade process. The proposed model incorporates variations of the multiplier distribution used to generate the process. Information theoretic principles are used to explain increased burstiness of network traffic. Kalman filter based techniques are developed for predicting traffic burstiness. The question of complexity of broadband traffic is also examined by defining a volatility parameter. Multifractal Based Network Traffic Modeling provides an overview of existing broadband traffic modeling based on the Poisson process and its variants like the MM1 models. Throughout the book, the authors have focused on the problem of broadband traffic modeling keeping in mind long range dependencies in broadband traffic. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781402075667
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