Postgraduate Course: Performance Modelling (Level 11) (INFR11082)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Not available to visiting students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Informatics
||Other subject area||None
||Taught in Gaelic?||No
|Course 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.
This course is identical to the level 10 version except for the assessed coursework and additional learning outcome.
Entry Requirements (not applicable to Visiting Students)
|Prohibited Combinations|| Students MUST NOT also be taking
Performance Modelling (Level 10) (INFR10046)
||Other requirements|| For Informatics PG and final year MInf 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.
|Additional Costs|| None
Course Delivery Information
|Delivery period: 2011/12 Semester 1, Not available to visiting students (SS1)
||WebCT enabled: No
|No Classes have been defined for this Course|
||Week 1, Tuesday, 12:10 - 13:00, Zone: Central. DHT 4.18 |
|Main Exam Diet S2 (April/May)||2:00|
Summary of Intended Learning Outcomes
|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 concepts of steady state and transient analysis. 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 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.
5 - Students will learn to abstract from extraneous detail and focus on the important aspects of a problem.
6 - Students will develop the ability to assimilate knowledge about different formalisms and tools and put them to practical use.
7 - Students will develop skills in analysing and interpreting presented data.
8 - Students will demonstrate their knowledge of the state of the art of a topic area covered in the course.
|Written Examination 75|
Assessed Assignments 25
Oral Presentations 0
The coursework is comprised of two practical exercises which exercise modelling skills in different formalisms. The second modelling coursework uses the stochastic process algebra PEPA.
||*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.
*High-level modelling languages: the stochastic process algebra PEPA, stochastic Petri nets and networks of queues.
*Recent developments and the state of the art. Research directions.
Relevant QAA Computing Curriculum Sections: Simulation and Modelling
||* 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.
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
|Course organiser||Dr Michael Rovatsos
Tel: (0131 6)51 3263
|Course secretary||Miss Kate Weston
Tel: (0131 6)50 2701
© Copyright 2011 The University of Edinburgh - 16 January 2012 6:17 am