Large Scale Networks : Modeling and Simulation

This book offers new insight on the identification of relationships and behaviours of Internet traffic, based on the study of the influence of the topological characteristics of a network upon the traffic behaviour. The content refers to complex systems that have a network-like structure. In a complex system, it is often the case that the utility of a structure or process is expressed at the next higher level of organization relative to the process itself. Complexity should express the level of interconnectedness and interdependencies of a system. The key issue to illustrate this inter-dependency is the self-similarity, which can be expressed both in the fractal-like topological structure of scale-free networks and in the characteristics of traffic flow in a packet-switched environment. In practice, this approach leads to develop good predictors of network performance.

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.