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
Back Cover