Lecture 1 - Network Simulation Basics

Mind Map: Network Simulation

Central Idea

• Network Simulation

• What is Simulation?

  • Imitation of the operation of a real-world process or system over time

Main Branches

1. Definition

• What is Network Simulation?

  • Technique to model network behaviour

  • Used for testing and analysis

2. Types of Network Simulators

• Discrete Event Simulators

  • Examples: NS2, NS3

• Packet-Level Simulators

  • Examples: OMNeT++, QualNet

• Hybrid Simulators

  • Combines features of discrete and packet-level

3. Applications

• Performance Evaluation

  • Throughput, latency, and packet loss analysis

• Network Design

  • Topology optimization

• Protocol Testing

  • Validate new protocols before deployment

• Security Analysis

  • Simulate attacks and defenses

4. Key Components

• Network Topology

  • Nodes, links, and their configurations

• Traffic Models

  • Types of traffic (e.g., TCP, UDP)

• Simulation Parameters

  • Time, scale, and metrics to measure

5. Tools and Software

• Popular Tools

  • GNS3, Cisco Packet Tracer

• Open-source Options

  • Mininet, OMNeT++

• Commercial Software

  • Riverbed Modeler, NetSim

6. Challenges

• Scalability

  • Handling large networks

• Accuracy

  • Real-world vs. simulated results

• Complexity

  • Managing intricate network behaviors

7. Future Trends

• Integration with AI

  • Machine learning for dynamic simulations

• Cloud-based Simulations

  • Remote access and collaboration

• 5G and IoT Simulations

  • New protocols and devices

Conclusion

• Network simulation is a vital tool for understanding and optimizing network performance, with diverse applications and ongoing advancements.

Review

• System vs. Model vs. Simulation

  • System = Group of objects that are joined together (relationships/interactions)

  • System environment = A system can be affected by changes occuring outside the system

  • Components of a system = Entity, Attribute, Activity, State, Event

  • Model = Representation of a real-world system

  • Generally composed of assumptions, some models are impossible to solve mathematically

  • Discrete Systems = Variables change only at discrete set of points in time

  • Continuous Systems = State variables change continuously in time

  

  • Stochastic/Deterministic: A deterministic model has no random variables (known input). Stochastic models have random variables.

  • Static/Dynamic: In a static model, time is not a significant variable (static is like a picture of the system at a given time).

  • Continuous/Discrete: In continuous systems, state variables evolve continuously.

    

• Simulation Software = – Packet Tracer

  – Riverbed (formerly OPNET)

  – NS-2, NS-3

  – OMNET++, GTnetS

  – CloudSim– Packet Tracer

  – Riverbed (formerly OPNET)

  – NS-2, NS-3

  – OMNET++, GTnetS

  – CloudSim