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Home » Simulation and Modelling

> # 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.

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