ContentPreface ................................................................................................................................. 4 1. State of the art and trends in informatic network modelling * 1.1 Self-similarity and fractals in traffic modelling 1.1.1 The building of a framework 1.1.2 Mathematical background of self-similar processes 1.2 Models of complex networks 1.3 Scale free networks 1.3.1 Basic properties of SFNs 1.3.2 Distances and bounds in SFN 1.4 Current trends in traffic flow and complex networks modelling 2. Flow traffic models 2.1 Background in traffic modelling 2.1.1 The definition of the informational traffic 2.1.2 Internet teletraffic modelling 2.1.3 Internet teletraffic engineering 2.1.4 Internet traffic times series modelling 2.2 Renewal traffic models 2.2.1 Poisson processes 2.2.2 Bernoulli processes 2.2.3 Phase-type Renewal processes 2.3 Markov traffic models 2.3.1 Markov-Modulated Traffic Models 2.2.2 Markov-Modulated Poisson process 2.3.3 Transition-Modulated Processes 2.4 Fluid traffic models 2.5 Autoregressive traffic models 2.5.1 Linear Autoregressive Models (AR) 2.5.2 Moving Average Series (MA) models 2.5.3 Autoregressive Moving Average Series (ARMA) models 2.5.4 Integrated Autoregressive Moving-Average (ARIMA) models 2.5.5 Fractional Integrated Autoregressive Moving-Average (FARIMA) models 2.6 TES traffic models 2.6.1 TES processes 2.6.2 The empirical TES methodology 2.7 Self-similar traffic models 3. Self similarity in traffic 3.1 Self-Similar Traffic and Network Performance 3.1.1 Quality of service and resource allocation 3.1.2 The concept of self-similarity 3.1.3 The effects of self-similarity on network performance 3.2 The mathematics of self-similar processes 3.2.1. Stationary random processes 3.2.2. Continuous time self-similar processes 3.2.3. Discrete time self-similar processes 3.2.4. Properties of the fractal processes 3.3 Self similar traffic modelling 3.3.1 Single source traffic models 3.3.2 Aggregate traffic models 3.3.3 Procedures for synthetic self-similar traffic generation 3.3.4 The Fast Fourier Transform method (FFT) 3.4 Evidence of self-similarity in real traffic 3.4.1 Rescaled range method 3.4.2 Dispersion-time analysis 3.4.3 Periodogram method 3.4.4 Whittle estimator 3.4.5 Wavelet based method 3.5 Application specific models 3.5.1 Internet application specific traffic models 3.5.2 Models for TCP flows 4. Topological models for complex networks 4.1 Topology of real networked: empirical results 4.1.1 World-Wide Web 4.1.2 The Internet 4.2 Random graph theory 4.2.1 Erdoes-Renyi model 4.2.2 Subgraphs 4.2.3 The evolution of the graph 4.2.4 Degree distribution 4.2.5 The connection degree (connectivity) and diameter 4.2.6 Clustering coefficient 4.2.7 Graph spectrum 4.3 Small-world networks 4.3.1 The Watts-Strogats (WS) model 4.3.2 Properties of small-world networks 4.4 The scale-free model 4.4.1 Definition of the scale-free model (SF) 4.4.2 Theoretical aspects 4.4.3 Limit cases of the SF model 4.4.4 Properties of the scale-free model 5. Topology and traffic simulations in complex networks 5.1 Example of building and simulating a network 5.1.1 Simple simulation example 5.2 The construction of complex network topologies 5.2.1 The construction of a random network 5.2.2 The construction of a small world network 5.2.3 The construction of a scale-free network 5.3 Analyses and topological comparisons of complex networks 5.4 Self-similar traffic simulation 5.5 Traffic simulation on combined topologies of networks and traffic sources 5.5.1 Details on the used topologies and traffic sources 5.5.2 Hurst parameter estimation results 5.5.3 The influence of topology upon the traffic 6. Case studies 6.1 Hurst exponent analysis on real traffic 6.1.1 Traffic capture 6.1.2 Graphical estimator graphics 6.2 Inferring statistical characteristics as an indirect measure of the quality of service 6.2.1 Defining an inference model 6.2.2 Highlighting network similarity 6.2.3 Case study - inter-domain characteristic interference 6.3 Modelling nonlinear phenomena in complex networks and detecting traffic anomalies 6.3.1 Introduction 6.3.2 Self-similarity characteristic of the informational traffic in networks 6.3.3 Using similarity in network management 1506.3.4 Test platform and processing procedure for traffic analysis 6.3.5 Discussion on experimental results of case studies 6.3.6 Recent trends in traffic self-similarity assessment for cloud data modelling.................................... 1586.4 Optimization of Quality of Services by monitoring cloud traffic.......................................................... 6.4.1 Monitoring the dynamics of network traffic in cloud .................................. 1646.4.2 Coping with traffic uncertainty for load balancing in cloud 1696.4.3 Wide-area data analysis for detection changes in traffic patterns 1726.4.4 Monitoring cloud services using NetFlow standard 1756.4.5 Implementing cloud services in the automation domain 1806.5 Developing and validating strategies for traffic monitoring on RLS models 1826.5.1 Simulation framework 1826.5.2 Algorithms ran in simulation 1866.5.3 Performance analysis 201References.............................................................................................................................. 197