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