Chapter 1 State of the art and trends in information networks modeling
1.1 Self-similarity and fractals in traffic modeling
1.1.2 Mathematical background of self-similar processes
1.1.2.1 Stable distributions and semistable processes
1.1.2.2 First empirical processes with power-law statistics
1.1.2.3 From semistability to self-similarity
1.1.2.4 Self-similarity versus long-range dependence
1.1.2.5 Self-similarity discovered in network traffic
1.2 Models of complex 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 modeling
2.1 Background in traffic modeling
2.1.1 Definition of the informational traffic
2.1.2 Internet teletraffic modeling
2.1.2.1 Introduction in teletraffic theory
2.1.2.2 Basic concepts of teletraffic theory
2.1.2.3 Teletraffic techniques
2.1.3 Internet teletraffic engineering
2.1.3.1 Big bandwidth philosophy
2.1.3.2 Managed bandwidth philosophy
2.1.3.3 Open-loop control of stream traffic
2.1.3.4 Closed-loop control of elastic traffic
2.1.4 Internet traffic times series modeling
2.2.3 Phase-type renewal processes
2.3.1 Markov-modulated traffic models
2.3.2 Markov-modulated Poisson process
2.3.3 Transition-modulated processes
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
2.6.2 Empirical TES methodology
2.7 Self-similar traffic models
Chapter 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 Concept of self-similarity
3.1.3 Effects of self-similarity on network performance
3.2 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
3.2.4.2 Slowly decaying dispersion
3.2.4.3 Heavy-tailed distributions
3.2.4.6 SRD versus LRD: Conclusion
3.3 Self-similar traffic modeling
3.3.1 Single-source traffic models
3.3.1.2 Log-normal distribution
3.3.2 Aggregate traffic models
3.3.2.2 Fractional Brownian motion
3.3.2.3 Fractional Gaussian noise
3.3.2.4 Fractional ARIMA processes (FARIMA)
3.3.2.5 Chaotic deterministic maps
3.3.3 Procedures for synthetic self-similar traffic generation
3.3.3.4 Random midpoint displacement (RMD) method
3.3.3.5 Wavelet transform-based method
3.3.3.6 Successive random addition (SRA)
3.3.4 Fast Fourier transform (FFT) method
3.4 Evidence of self-similarity in real traffic
3.4.2 Dispersion–time analysis
3.5 Application specific models
3.5.1 Internet application-specific traffic models
3.5.2.1 TCP flows with congestion avoidance
3.5.2.2 TCP flows with active queue management
Chapter 4 Topological models of complex networks
4.1 Topology of large-scale networks
4.2 Main approaches on networks topology modeling
4.2.2.1 Average trajectory length
4.2.2.2 Clustering coefficient
4.2.2.3 Degree of distribution
4.2.3 The scale-free (SF) model
4.2.3.1 Definition of the scale-free network (SFN) model
4.2.3.2 General SFN properties
4.2.3.3 Diameter of scale-free networks
4.2.3.4 Average trajectory length
4.2.3.5 Node degree correlation
4.2.3.6 Clustering coefficient
4.3 Internet traffic simulation with scale-free network models
4.3.2.1 Scale-free network design algorithm
4.3.3 Simulation results and discussion
4.4 Improvement of Internet traffic QoS performance
4.4.1 Quality of service (QoS) requirements in highspeed networks
4.4.2 Technologies for high-speed networks
4.4.2.1 Characteristics of OBS networks
4.4.2.2 Burst assembly schemes
4.4.3 Traffic-smoothing using burst assembly schemes
4.4.3.1 Analysis of assembled traffic
4.4.3.2 Description of the proposed burst assembly algorithm
4.5 Fractal approaches in Internet traffic simulation
4.5.1.1 Parallel platform model
4.5.1.2 Share-scheduling policy for parallel processing
4.5.2 Evidence of attractors in large-scale networks models
Chapter 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 Construction of complex network topologies
5.2.1 Construction of a random network
5.2.2 Construction of a small-world network
5.2.3 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 Influence of topology upon the traffic
6.1 Hurst exponent analysis on real traffic
6.1.2 Graphical estimators representation
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: Interdomain characteristic interference
6.3 Modeling nonlinear phenomena in complex networks and detecting traffic anomalies
6.3.2 Self-similarity characteristic of the informational traffic in networks
6.3.2.1 SS: Self-similar processes
6.3.3 Using similarity in network management
6.3.3.1 Related work on anomalies detection methods
6.3.3.2 Anomaly detection using statistical analysis of SNMP–MIB
6.3.3.3 Subnetwork similarity testing
6.3.4 Test platform and processing procedure for traffic analysis
6.3.4.3 Network federalization
6.3.5 Discussion on experimental results of case studies
6.3.5.1 Implementing and testing an SFN model for Internet
6.3.5.2 Detecting network anomalies
6.3.6 Recent trends in traffic self-similarity assessment for Cloud data modeling
6.3.6.1 Anomalies detection in Cloud
6.3.6.2 Combining filtering and statistical methods for anomaly detection
6.4 Optimization of quality of services by monitoring Cloud traffic
6.4.1 Monitoring the dynamics of network traffic in Cloud
6.4.1.2 Algorithm for capturing cross-deployment workload changes
6.4.1.3 Provisioning planning algorithm
6.4.2 Coping with traffic uncertainty for load balancing in Cloud
6.4.3 Wide-area data analysis for detection changes in traffic patterns
6.4.4 Monitoring Cloud services using NetFlow standard
6.4.4.2 Packet-sampled flow data
6.4.5 Implementing Cloud services in the automation domain
6.5 Developing and validating strategies for traffic monitoring on RLS models
6.5.2 Algorithms ran in simulation