Modelling and Simulation (Level 11) (P00898)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-MS

This course teaches various aspects of computer-aided modelling for performance evaluation of dynamic systems. The emphasis is on computer systems and communication networks; however other dynamic systems such as manufacturing systems will also be considered. The central concept of the course will be that a model, as well as being an abstract representation of a system, is a tool which we can exploit to derive information about the system. The more detail we invest in the model, the more sophisticated the information we can extract from it. As the course progresses the models will become increasingly detailed; the corresponding solution techniques will similarly become more complex, relying on increasing levels of computer assistance and visualisation.

Entry Requirements

? Pre-requisites : For Informatics PG students only, or by special permission of the School. The only formal pre-requisite is a second level Mathematics course providing knowledge of elementary probability and statistics.

? Prohibited combinations : Modelling & Simulation (Level 10)

Subject Areas

Home subject area

Computer Systems Organisation, (School of Informatics, Schedule O)

Other subject areas

Theoretical Computer Science, (School of Informatics, Schedule O)

Computational Systems Biology, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
18/09/2007	12:10	13:00	Room G17, Adam Ferguson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	12:10	13:00	Central
Lecture	Friday	12:10	13:00	Central

Summary of Intended Learning Outcomes

The course fosters a basic competency in performance modelling using both Markov processes and simulation. In particular, at the end of the course there should be several learning outcomes:

-understanding the key ideas of performance modelling and the trade-offs between timeliness and efficient use of resources and be able to demonstrate this by an ability to give an account of these ideas and explain why the trade-off occurs.
-know the operational laws and be able to apply them to any system which satisfies the appropriate conditions to derive further information about the system. Furthermore students will be able to assess from a system description whether the conditions are met.
-the ability to design, construct and solve a performance model based on a Markov process in various high-level modelling formalisms as well as directly at the state transition level. Moreover students will be able to give an account of the underlying mathematics and the concept of steady state. The students should understand, and be able to give an account of, the assumptions which must be made about a system in order to model it as a Markov process.
-the ability to design, construct and solve a substantial performance model based on simulation, and instrument that model in order to derive performance measures. Moreover students will be able to judge the impact of the theory of simulation and statistical issues when interpreting the results from simulation models.

The case study work within the course allows students to develop the skills to analyse a system description and abstract from it to create a model with an appropriate level of detail.

Students also develop judgement with respect to choosing an appropriate modelling technique for a given scenario, so that when given a description of a problem, and the resources and skills available, they are able to recommend the best-suited modelling formalism and solution technique.