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This book offers a rigorous analysis of the achievements in the field of traffic control in large networks, oriented on two main aspects: the selfsimilarity in traffic behaviour and the scalefree characteristic of a complex network. Additionally, the authors propose a new insight in understanding the inner nature of things, and the causeandeffect 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 selfsimilarity, perceived as an adaptability of traffic in networks. However, the problem with selfsimilar models is that they are computationally complex. Their fitting procedure is very timeconsuming, while their parameters cannot be estimated based on the online measurements. In this aim, the main objective of this book is to discuss the problem of traffic prediction in the presence of selfsimilarity 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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This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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