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Large Scale Networks Modeling And Simulation Radu Dobrescu Author

  • SKU: BELL-5679840
Large Scale Networks Modeling And Simulation Radu Dobrescu Author
$ 31.00 $ 45.00 (-31%)

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Large Scale Networks Modeling And Simulation Radu Dobrescu Author instant download after payment.

Publisher: CRC Press
File Extension: PDF
File size: 12.29 MB
Pages: 276
Author: Radu Dobrescu (Author), Florin Ionescu
ISBN: 9781498750172, 1498750176
Language: English
Year: 2016

Product desciption

Large Scale Networks Modeling And Simulation Radu Dobrescu Author by Radu Dobrescu (author), Florin Ionescu 9781498750172, 1498750176 instant download after payment.

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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