Lecture 1 - Network Simulation Basics

Network Simulation

Technique to model network behaviour for testing and analysis.

Discrete Event Simulators

Simulators that model events occurring at specific points in time (e.g., NS2, NS3).

Packet-Level Simulators

Simulators that focus on the behavior of packets in a network (e.g., OMNeT++, QualNet).

Hybrid Simulators

Simulators that combine features of both discrete event and packet-level simulators.

Performance Evaluation

An application of network simulation that analyzes throughput, latency, and packet loss.

Network Design

Application of network simulation for optimizing network topology.

Protocol Testing

Validating new protocols before their deployment using network simulation.

Security Analysis

Simulating attacks and defenses to assess network security.

Network Topology

The arrangement of nodes, links, and their configurations in a network.

Traffic Models

Representations of different types of network traffic, such as TCP and UDP.

Simulation Parameters

Variables like time, scale, and metrics used to measure simulation outcomes.

GNS3

A popular tool for network simulation.

Cisco Packet Tracer

A widely used network simulation software for educational purposes.

Open-source Options

Free network simulation tools like Mininet and OMNeT++.

Commercial Software

Paid network simulation tools such as Riverbed Modeler and NetSim.

Scalability

The challenge of managing large networks in simulations.

Accuracy

The degree to which simulated results reflect real-world outcomes.

Complexity

The challenge of managing intricate behaviors in network simulations.

Integration with AI

Future trend involving machine learning for dynamic network simulations.

Cloud-based Simulations

Remote access and collaboration in network simulation environments.

5G and IoT Simulations

Future trend focusing on new protocols and devices in network simulations.

System

A group of objects joined together through relationships and interactions.

Model

A representation of a real-world system, often based on assumptions.

Discrete Systems

Systems where variables change only at specific points in time.

Continuous Systems

Systems where state variables change continuously over time.

Stochastic Models

Models that include random variables and uncertainty.

Deterministic Models

Models with known inputs and no random variables.

Static Models

Models where time is not a significant variable, representing a snapshot in time.

Dynamic Models

Models where time plays a crucial role in the system's behavior.