Modeling and Simulation of Complex Communication Networks

Cover
Contents
Preface
Part I Modeling and simulation
1 Modeling and simulation: the essence and increasing importance
1.1 Introduction
1.2 Experimentation aspects of simulation
1.3 Experience aspects of simulation
1.3.1 Simulation for training
1.3.2 Simulation for entertainment
1.4 Taxonomies and ontologies of simulation
1.4.1 Background
1.4.2 Taxonomies of simulation
1.4.3 Ontologies of simulation
1.5 Evolution and increasing importance of simulation
1.6 Conclusion
Disclaimer
Appendix A – A list of over 750 types of simulation
Appendix B – A list of 120 types of input
References
2 Flexible modeling with Simio
2.1 Overview
2.2 Simio object framework
2.3 Simio object classes
2.4 Modeling movements
2.5 Modeling physical components
2.6 Processes
2.7 Data tables
2.8 Experimentation with the model
2.9 Application programming interface
2.10 Applications in scheduling
2.11 Summary
Glossary
References
3 A simulation environment for cybersecurity attack analysis based on network traffic logs
3.1 Introduction
3.1.1 Network simulation
3.1.2 Network emulation
3.1.3 The application of network simulation and emulation in network security
3.1.4 Virtualization
3.1.5 Virtualization using hypervisor
3.1.6 Virtualization using container
3.1.7 Virtual machines and simulation
3.2 Literature review
3.2.1 Network anomalies and detection methods
3.2.2 Network workload generators
3.2.3 Network simulation for security studies
3.3 Methodology
3.4 Defining a simulated and virtualized test bed for network anomaly detection researches
3.4.1 GNS3
3.4.2 Ubuntu
3.4.3 Network interfaces
3.5 Simulated environment for network anomaly detection researches
3.5.1 Victim machine
3.5.2 Attacker machine
3.5.3 pfSense firewall
3.5.3.1 Firewall configuration
3.5.4 NAT and VMware host-only networks
3.5.5 Traffic generator machine
3.5.6 NTOPNG tool
3.5.6.1 NTOPNG configuration
3.5.6.2 NTOPNG configuration to dump logs to Mysql machine
3.5.7 Repository machine
3.5.7.1 Repository machine configuration
3.5.7.2 Give a remote root access to Data_ Repository machine
3.6 Discussion and results
3.7 Summary
References
Part II Surveys and reviews
4 Demand–response management in smart grid: a survey and future directions
4.1 Overview
4.2 Introduction
4.3 Backgrounds
4.3.1 Smart grid
4.3.2 Demand–response management
4.3.3 Complex systems
4.3.4 Learning-based approaches
4.4 A review of demand–response management in SG
4.4.1 Learning-based approaches
4.4.1.1 Artificial neural network
4.4.1.2 Reinforcement learning approach
4.4.2 Complex system
4.4.2.1 Collaborative approach
4.4.2.2 Complex adaptive system
4.4.2.3 Demand-side integration
4.4.2.4 Particle swarm optimization
4.4.2.5 Game-theory approach
4.4.3 Other techniques
4.4.3.1 Security management
4.4.3.2 Home-energy management system
4.4.3.3 Electric vehicles charging
4.4.3.4 Renewable energy sources
4.4.3.5 Energy market
4.4.3.6 Mircorgrid
4.5 Open-research problems and discussion
4.5.1 Open-research problems in learning system
4.5.2 Open-research problems in complex system
4.5.3 Open-research problems in other techniques
4.6 Conclusions
References
5 Applications of multi-agent systems in smart grid: a survey and taxonomy
5.1 Overview
5.2 Introduction
5.3 A review of multi-agent system to smart-grid application
5.3.1 Communication management
5.3.1.1 Group communication
5.3.1.2 Learning-based approach
5.3.2 Demand–response management
5.3.2.1 Learning-based approach
5.3.2.2 Complex system
5.3.3 Fault monitoring
5.3.3.1 Self-organizing
5.3.3.2 Algorithmic approach
5.3.4 Power scheduling
5.3.4.1 Complex system
5.3.4.2 Learning-based approach
5.3.5 Storage and voltage management
5.3.5.1 Learning
5.3.5.2 Monitoring
5.3.5.3 Searching
5.4 Open research problems and discussion
5.5 Conclusions
References
6 Shortest path models for scale-free network topologies: literature review and cross comparisons
6.1 Mapping the Internet topology
6.1.1 Interface level
6.1.1.1 Active methodology based on traceroute
6.1.1.2 IP options and subnet discovery
6.1.2 Router level
6.1.2.1 Alias resolution techniques
6.1.2.2 Recursive router discovery
6.1.3 AS level
6.1.3.1 Passive methodology based on BGP and Internet Routing Registry
6.1.3.2 Active methodology based on traceroute
6.1.4 Geographic network topologies
6.2 Internet models based on the graph theory
6.2.1 Fundamental notions from the graph theory
6.2.2 Topology models
6.2.2.1 Regular and well-known topology models
6.2.2.2 Random and small-world topology model
6.2.2.3 Power-law topology models
6.2.2.4 Scale-free topology model
6.2.2.5 Hierarchical methods
6.2.3 Topology generator tools
6.2.3.1 Random topology generator tools
6.2.3.2 Power-law topology generator tools
6.2.3.3 Scale-free topology generator tools
6.2.3.4 Hierarchical topology generator tools
6.3 Shortest path models
6.3.1 Parameters definition
6.3.2 Shortest path models
6.3.2.1 Gamma distribution
6.3.2.2 Weibull distribution
6.3.2.3 Lognormal distribution
6.3.3 Cross-comparison among shortest path models
6.3.4 Shortest path models applications
6.4 Conclusion
Acknowledgment
References
Part III Case studies and more
7 Accurate modeling of VoIP traffic in modern communication
7.1 Introduction
7.2 Modern communication networks: from simple packet network to multiservice network
7.3 Voice over IP (VoIP) and quality of service (QoS)
7.3.1 Basic structure of a VoIP system
7.3.2 VoIP frameworks: H.323 and SIP
7.3.2.1 H.323
7.3.2.2 Session initiation protocol
7.3.3 Basic concepts of QoS
7.3.4 QoS assessment
7.3.5 Oneway delay
7.3.6 Jitter
7.3.7 Packetloss rate
7.4 Self-similarity processes in modern communication networks
7.4.1 Self-similar processes
7.4.2 Haar wavelet-based decomposition and Hurst index estimation
7.5 QoS parameters modeling on VoIP traffic
7.5.1 Jitter modeling by self-similar and multifractal processes
7.5.2 Packet-loss modeling by Markov models
7.5.3 Packet-loss simulation and proposed model
7.6 Conclusions
References
8 Exploratory and validated agent-based modeling levels case study: Internet of Things
8.1 Introduction
8.1.1 Agent-based modeling framework
8.1.1.1 Exploratory agent-based level
8.1.2 Agent-based simulator
8.1.2.1 Simulator: NetLogo
8.1.2.2 Research questions
8.1.3 Case study: 5G networks and Internet of Things
8.1.3.1 Modeling approach and design
8.1.3.2 Implementation
8.1.4 Results and discussion
8.1.4.1 Simulation parameters
8.1.4.2 Behavior space experiments
8.1.4.3 Descriptive statistics
8.1.4.4 Discussion
8.1.5 Conclusion
8.2 Validated agent-based modeling level case study: Internet of Things
8.2.1 Introduction
8.2.2 Validated agent-based level
8.2.2.1 Validation techniques
8.2.2.2 Virtual overlay multi-agent system
8.2.2.3 Research questions
8.2.3 Case study: 5G networks and Internet of Things
8.2.3.1 Modeling approach and design
8.2.3.2 Basic simulation model
8.2.3.3 IoT creation module
8.2.3.4 Basic IoT module
8.2.3.5 VOMAS agent design
8.2.4 Results and discussion
8.2.4.1 Simulation parameters
8.2.5 Validation discussion
8.2.6 Conclusion
References
9 Descriptive agent-based modeling of the “Chord” P2P protocol
9.1 Introduction
9.2 Background and literature review
9.2.1 CAS literature
9.2.2 Modeling and simulation of CACOONS
9.2.2.1 Agent-based modeling
9.2.2.2 Complex network modeling
9.2.3 Chord P2P protocol
9.2.3.1 Architecture and working
9.2.4 Hashing and key mapping
9.2.5 Node joining
9.2.6 Finger table
9.2.7 Stabilization
9.2.8 Performance of chord
9.2.9 PeerSim
9.2.10 Literature review
9.2.10.1 Security-based chord
9.2.10.2 Peer data management-based chord
9.2.10.3 Mobility-based chord
9.2.10.4 Hierarchy-based chord
9.2.10.5 Routing and latency-based chord
9.2.10.6 Load distribution and resource allocation based Chord
9.2.10.7 Other chord-based approaches
9.3 ODD model of a “Chord”
9.3.1 Purpose
9.3.2 Entities, state variables, and scales
9.3.2.1 Agents/Individuals
9.3.2.2 Spatial units
9.3.2.3 Environment
9.3.2.4 Collectives
9.3.3 Process overview and scheduling
9.3.4 Design concepts
9.3.4.1 Basic principles
9.3.4.2 Emergence
9.3.4.3 Adaptation
9.3.4.4 Objectives
9.3.4.5 Learning
9.3.4.6 Sensing
9.3.4.7 Stochasticity
9.3.4.8 Interaction
9.3.4.9 Collectives
9.3.4.10 Observation
9.3.5 Initialization
9.3.6 Input data
9.3.7 Sub-models
9.3.7.1 Set-up
9.3.7.2 Init-node
9.3.7.3 Create-network
9.3.7.4 Go
9.4 DREAM model of a “Chord”
9.4.1 Agent design
9.4.1.1 State charts (of agents)
9.4.2 Activity diagrams
9.4.3 Flowchart
9.4.4 Pseudo-code based specification
9.4.4.1 Agents and breed
9.4.4.2 Globals
9.4.4.3 Procedures
9.4.4.4 Experiments
9.5 Results and discussion
9.5.1 Metrics (table and description)
9.5.2 PeerSim results
9.5.3 ABM results
9.5.4 Comparison of PeerSim and ABM
9.5.5 DREAM network models
9.5.5.1 Plots of centralities
9.5.5.2 Plots of centralities using power-law
9.5.6 Discussion (ODD vs. DREAM pros and cons of both) and which is more useful for modeling the chosen P2P protocol
9.5.7 Chord and theory of computation
9.5.7.1 Complexity theory
9.6 Conclusions and future work
References
10 Descriptive agent-based modeling of Kademlia peer-to-peer protocol
10.1 Introduction
10.2 Background and literature review
10.2.1 Complex adaptive systems
10.2.2 Cognitive agent-based computing
10.2.3 Complex network modeling
10.2.4 Architecture of the “Kademlia” protocol
10.2.4.1 Introduction
10.2.4.2 System description
10.2.4.3 Distance calculation
10.2.4.4 Node
10.2.4.5 Protocol
10.2.4.6 Node Look up
10.2.4.7 Routing table
10.2.5 Literature review
10.3 Model design
10.3.1 ODD model of “Kademlia”
10.3.2 Overview
10.3.3 Design concept
10.3.4 Details
10.3.5 Activity diagrams of “Kademlia”
10.3.6 DREAM model of “Kademlia”
10.3.7 Network model
10.3.8 Pseudo-code description
10.4 Results and discussion
10.4.1 Evaluation metrics
10.4.2 Power law plots of centrality measures
10.4.3 PeerSim simulation using existing code in PeerSim
10.4.4 ABM simulation
10.4.4.1 Configuration
10.4.4.2 Results
10.4.5 Comparison of PeerSim and ABM results
10.4.6 Discussion
10.4.6.1 Comparison of ODD and DREAM
10.4.6.2 Kademlia relation with theory of computation
10.5 Conclusion and future work
References
11 Descriptive agent-based modeling of the “BitTorrent” P2P protocol
11.1 Introduction
11.1.1 Contributions
11.2 Background and literature review
11.2.1 Complex adaptive systems
11.2.2 Modeling and simulation of CACOONS
11.2.2.1 Agent-based modeling
11.2.2.2 Cognitive agent-based computing
11.2.2.3 Complex network modeling
11.3 BitTorrent peer-to-peer protocol
11.3.1 BitTorrent history overview
11.3.2 Content publishing in BitTorrent
11.3.3 Joining swarm and peers discovery in BitTorrent
11.3.4 Delivery procedure BitTorrent
11.3.5 BitTorrent architecture and working
11.3.5.1 Peer
11.3.5.2 Swarm
11.3.5.3 Tracker
11.3.5.4 Leecher
11.3.5.5 Seeder
11.3.5.6 Mechanism and architecture
11.3.5.7 Limitations of BitTorrent
11.4 BitTorrent literature review
11.4.1 PeerSim
11.4.1.1 Scalability
11.4.1.2 Modularity
11.5 Model design
11.5.1 ODD approach
11.5.1.1 Entities, state variables and scales
11.5.1.2 Process overview and scheduling
11.5.1.3 Design concepts
11.5.2 Overview of the proposed model
11.5.2.1 Problem statement
11.5.2.2 Node agents
11.5.2.3 States of node agents
11.5.2.4 Activity diagrams
11.5.2.5 Sequence diagrams
11.5.3 DREAM model
11.5.4 Pseudocode-based specification
11.5.4.1 Agents and breeds
11.5.5 Globals
11.5.6 Procedures
11.5.6.1 Check-if-segment-is-available
11.5.6.2 Check-if-segment-is-needed-by-others
11.5.6.3 Do-plots
11.5.6.4 Generate-random-segment-number
11.5.6.5 Go
11.5.6.6 Make-turtles
11.5.6.7 Makes-new-seeds-green
11.5.6.8 Selfish-green-turtles-dropout
11.5.6.9 Setup
11.5.6.10 Upload-file-segment
11.5.7 Experiments
11.5.8 Results and discussions
11.5.8.1 Metrics table and description
11.5.9 PeerSim results
11.5.10 ABM results
11.5.11 Comparison of both
11.5.12 DREAM network models
11.5.12.1 Plots of centralities
11.6 Discussion (ODD vs DREAM)
11.7 Conclusion
References
12 Social networks—a scientometric visual survey
12.1 Introduction
12.2 Background
12.2.1 Social networks—an overview
12.2.2 Citation networks
12.2.3 Co-citation networks
12.2.4 Bibliographic coupling
12.2.5 Coauthorship networks
12.2.6 Co-occurrence networks
12.3 Materials and methods
12.3.1 Data collection
12.3.2 CiteSpace—a science mapping tool
12.4 Results and discussion
12.4.1 Cited-references co-citation network analysis
12.4.1.1 Identification of largest cluster in cited references
12.4.2 Authors collaboration network analysis
12.4.3 Institution collaboration network analysis
12.4.4 Country collaboration network analysis
12.4.5 Keywords co-occurrence network analysis
12.4.6 Category co-occurrence network analysis
12.4.7 Journal co-citation network analysis
12.5 Summary of results
12.6 Conclusions and future work
References
Index
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