School of Informatics Course Descriptor

Course Descriptor

Course: Modelling and Simulation (Level 10)

Credit Points 10

Credit Level 10

Acronym INF-4-MS

Study Pattern Study Format Hours

Lectures 20

Tutorials 0

Timetabled Laboratories 0

Non-timetabled assessed assignments 30

Private Study/Other 50

Total 100

Pre-requisite Courses

Other Pre-requisite Requirements Successful completion of Year 3 of an Informatics Single or Combined Honours Degree, or equivalent by permission of the School. The only formal pre-requisite is a second level Mathematics course providing knowledge of elementary probability and statistics.

Co-requisites/Forbidden Combinations Modelling and Simulation (Level 11)

Credit Points	10
Credit Level	10
Acronym	INF-4-MS
Study Pattern	Study Format	Hours
Lectures	20
Tutorials	0
Timetabled Laboratories	0
Non-timetabled assessed assignments	30
Private Study/Other	50
Total	100

Short Description

This course teaches various aspects of computer-aided modelling for performance evaluation of (stochastic) dynamic systems. The emphasis is on stochastic modeling of 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.

Summary of Intended Learning Outcomes

Outcome
1.	Students will understand the key ideas of performance modelling and the trade-offs between timeliness and efficient use of resources. They will be able to demonstrate this by an ability to give an account of these ideas and explain why the trade-off occurs.
2.	Students will 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 they will be able to assess from a system description whether the conditions are met.
3.	They will have the ability to design, construct and solve a simple performance model based on a Markov process in various high-level modelling formalisms as well as directly at the state transition level. Moreover they 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.
4.	Students will also have the ability to design, construct and solve a simple performance model based on simulation, and instrument that model in order to derive performance measures.
5.	The case study work within the course allows the students to develop the skills to analyse a system description and abstract from it to create a model with an appropriate level of detail.
6.	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.
7.	Abstracting extraneous detail and focusing on the important aspects of a problem.
8.	The ability to assimilate knowledge about different formalisms and tools and put them to practical use.
9.	Skills in analysing and interpreting presented data.
10.	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: The module aims to foster several transferable skills:

Assessment Weightings (%)	Assessment	%
	Written Examination	75
	Assessed Assignments	25
	Oral Presentations	0

Assessment

The coursework is comprised of two practical exercises. The first involves the development of simple models of three described systems. The first, an E-commerce system is modelled using operational analysis, the second, a network buffer, is modelled as a Markov process and the third, a manufacturing system, is modelled as a GSPN. In the second practical students are given a detailed simulation model of an E-commerce system which they have to validate and verify before using it for experimentation on the configuration of the system.

Syllabus

Modelling and performance evaluation: models as tools; equilibrium and transient behaviour. Revision of basic probability concepts.

Making use of models: deriving performance measures from an equilibrium distribution; choosing the parameters for a model; measurement and workload modelling; experimentation.

Representing systems directly as analytic models: operational laws such as Little's Law, simple queues and Markov processes; solving equations to find equilibrium behaviour.

More complex Markov processes: stochastic process algebra, stochastic Petri nets and networks of queues.

Simulation modelling: introduction and motivation; the process view of simulation; SimJava; event scheduling and queues; statistics, reporting and random numbers.

Verification and validation of models; interpretation of results.

Relevant QAA Computing Curriculum Sections: Simulation and Modelling

Reading List

M. Ajmone Marsan, et al, 'Modelling with Generalized Stochastic Petri Nets', Wiley, 1995.
R. Jain, 'The Art of Computer Systems Performance Analysis', Wiley, 1991.
W.J. Stewart, 'Numerical Solutions of Markov Chains', Princeton University Press, 1995.
I. Mitrani, 'Probabilistic Modelling', Cambridge University Press, 1998.
C. Lindemann, 'Performance Modelling with Deterministic and Stochastic Petri Nets', Wiley 1998.

Last updated 17 April 2008 by ito

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Pre-requisite Courses
Other Pre-requisite Requirements	Successful completion of Year 3 of an Informatics Single or Combined Honours Degree, or equivalent by permission of the School. The only formal pre-requisite is a second level Mathematics course providing knowledge of elementary probability and statistics.
Co-requisites/Forbidden Combinations	Modelling and Simulation (Level 11)