Postgraduate Course: Performance Modelling (Level 11) (INFR11082)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course teaches various aspects of computer-aided modelling for performance evaluation of (stochastic) dynamic systems. The main focus is on stochastic modelling of computer systems and communication networks to assess performance characteristics such as throughput, response time etc.; however other dynamic systems such as manufacturing systems may also be considered. The central concept of the course will be that a model is as an abstract representation of a system which can be used as a tool to derive information about dynamic behaviour of 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 model will become increasingly detailed; the corresponding solution techniques will similarly become more complex, relying on increasing levels of computer assistance. |
| Course description |
*Modelling and performance evaluation: models as tools; equilibrium and transient behaviour; analytic vs. algorithmic models. 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.
* Representing systems as algorithmic models: process-oriented and event-oriented simulation, variance reduction and stopping conditions.
*High-level modelling languages: the stochastic process algebra PEPA, stochastic Petri nets and networks of queues.
Relevant QAA Computing Curriculum Sections: Simulation and Modelling
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
- A solid background in mathematical thinking is required.
- Probability theory will be used extensively: Random variables, expectation, Markov chains, exponential distributions, joint and conditional probabilities.
- Basic linear algebra.
- A basic level of understanding of the architecture and operation of computer systems and networks. |
Information for Visiting Students
| Pre-requisites | Visiting students are required to have comparable background to that
assumed by the course prerequisites listed in the Degree Regulations &
Programmes of Study. If in doubt, consult the course lecturer. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2017/18, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
|
| Assessment (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
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.
You should expect to spend approximately 30 hours on the coursework for this course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- 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. 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
- 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 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
- Students will develop a basic understanding of simulation and the difference between algorithmic and analytic modelling. They will appreciate the components of the simulation engine and the importance that they are implemented efficiently
- Students will 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. Students will also learn to abstract from extraneous detail and focus on the important aspects of a problem, and understand the importance of matching the model to the question to be answered
- Students will develop the ability to assimilate knowledge about different formalisms and tools and put them to practical use. They will also develop skills in analysing and interpreting presented data
|
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. |
Contacts
| Course organiser | Dr Jane Hillston
Tel: (0131 6)50 5199
Email: |
Course secretary | Mr Gregor Hall
Tel: (0131 6)50 5194
Email: |
|
© Copyright 2017 The University of Edinburgh - 6 February 2017 8:09 pm
|
University Homepage