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Large Scale Networks

Name: Large Scale Networks
Brand: Taylor & Francis Inc
SKU: 9781498750172
Price: 248.69 EUR
Availability: PreOrder

Автор: Radu Dobrescu, Florin Ionescu

Език:

Корици: С твърди корици

Издател: Taylor & Francis Inc

Наличност: Очаква се допечатка

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248.69 € 486.40 лв

This book offers a rigorous analysis of the achievements in the field of traffic control in large ne...

Информация за книгата

Автор

Radu Dobrescu, Florin Ionescu

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2016

страници

286

EAN

9781498750172

ISBN

9781498750172

Enbook ID

10038320

Издател

Taylor & Francis Inc

Теглоt

598

Размери

378 x 164 x 22

Пълно описание

This book offers a rigorous analysis of the achievements in the field of traffic control in large networks, oriented on two main aspects: the self-similarity in traffic behaviour and the scale-free characteristic of a complex network. Additionally, the authors propose a new insight in understanding the inner nature of things, and the cause-and-effect based on the identification of relationships and behaviours within a model, which is based on the study of the influence of the topological characteristics of a network upon the traffic behaviour. The effects of this influence are then discussed in order to find new solutions for traffic monitoring and diagnosis and also for traffic anomalies prediction.

Although these concepts are illustrated using highly accurate, highly aggregated packet traces collected on backbone Internet links, the results of the analysis can be applied for any complex network whose traffic processes exhibit asymptotic self-similarity, perceived as an adaptability of traffic in networks. However, the problem with self-similar models is that they are computationally complex. Their fitting procedure is very time-consuming, while their parameters cannot be estimated based on the on-line measurements. In this aim, the main objective of this book is to discuss the problem of traffic prediction in the presence of self-similarity and particularly to offer a possibility to forecast future traffic variations and to predict network performance as precisely as possible, based on the measured traffic history.

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