Simulation and Modelling

Lecturer s : Anthony Field (homepage) , Jeremy Bradley (homepage)
For course notes click on the lecturers homepages.

Objectives

This course provides an introduction to system modelling using both computer simulation and mathematical techniques. A range of case studies are examined, both in the lectures and tutorial exercises. The application areas considered are wide-ranging, although the emphasis is on the analysis of computer and communication systems using a variety of modelling paradigms such as simulation, queueing theory, stochastic process algebras and stochastic Petri nets. The course is self-contained, both in terms of notes and supporting software.

Contents

Introduction and motivation.

Modelling principles: Fundamental laws, Monte Carlo simulation, stochastic state transition systems.

Discrete-event simulation (DES): Principles of DES, formalisation as a Generalized Semi-Markov Processes (GSMPs), random number generation, distribution sampling, analysis of simulation output.

Markov Processes (MPs): Numerical solution of MPs, analytical solution of MPs.

High-level formalisms, tools and techniques: Stochastic Petri Nets (SPNs) and Stochastic Process Algebras (SPAs), generation of the underlying MP, Queueing Networks.

Stochastic simulation in biochemical modelling.

Learning Outcomes

By the end of the course students will be able to apply the fundamental laws of performance analysis to establish the relationships between workload parameters and system performance for a given system. They will also be able to develop performance models for simlpe real-world systems and will be able to solve those models to obtain meaningful performance measures. They will thus be able to analyse system responsiveness, scalability etc. as a function of workload.