Simulation and Modelling
Suggested Books: • Discrete-event System Simulation J. Banks, J.S. Carson and B.L. Nelson Prentice Hall International, 1994
SIMULATION AND MODELLING
Tony Field and Jeremy Bradley {ajf,jb}@doc.ic.ac.uk
• Simulation Modeling and Analysis A.M. Law and W.D. Kelton McGraw Hill, 2000 • Probabilistic Modelling I. Mitrani Cambridge University Press, 1998
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2
• A Compositional Approach to Performance Modelling (first three chapters) J. Hillston Cambridge University Press, 1996. On-line at: http://www.doc.ic.ac.uk/ jb/teaching/336/1994hillston-thesis.ps.gz • Probability and Statistics with Reliability, Queuing and Computer Science Applications K. Trivedi Wiley, 2001
Course Overview
• This course is about using models to understand performance aspects of real-world systems • Models have many uses, typically:
– To understand the behaviour of an existing system (why does my network performance die when more than 10 people are at work?)
– To predict the effect of changes or upgrades to the system (will spending £100,000 on a new switch cure the problem?) – To study new or imaginary systems (let’s bin the Ethernet and design our own scalable custom routing network!)
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4
• There are many application areas, e.g. – Computer architecture – Networks/distributed systems – Software performance engineering – Telecommunications – Manufacturing – Healthcare – Transport – Finance – Environmental science/engineering • Here, we’ll focus on the important common principles, rather than become entrenched in specific application areas
Some Important Questions
• What are we trying to measure? • How do we measure it? • What sorts of model can we build? • Is the model well-defined? • How can we “solve” the model? • What formalisms can we use to help define a model? • How does the model compare to the real system?
5
6
The Focus...
• We’re going to focus on discrete event systems, with continuous time and discrete states • Our models will essentially be
SIMULATION AND MODELLING
Tony Field and Jeremy Bradley {ajf,jb}@doc.ic.ac.uk
• Simulation Modeling and Analysis A.M. Law and W.D. Kelton McGraw Hill, 2000 • Probabilistic Modelling I. Mitrani Cambridge University Press, 1998
1
2
• A Compositional Approach to Performance Modelling (first three chapters) J. Hillston Cambridge University Press, 1996. On-line at: http://www.doc.ic.ac.uk/ jb/teaching/336/1994hillston-thesis.ps.gz • Probability and Statistics with Reliability, Queuing and Computer Science Applications K. Trivedi Wiley, 2001
Course Overview
• This course is about using models to understand performance aspects of real-world systems • Models have many uses, typically:
– To understand the behaviour of an existing system (why does my network performance die when more than 10 people are at work?)
– To predict the effect of changes or upgrades to the system (will spending £100,000 on a new switch cure the problem?) – To study new or imaginary systems (let’s bin the Ethernet and design our own scalable custom routing network!)
3
4
• There are many application areas, e.g. – Computer architecture – Networks/distributed systems – Software performance engineering – Telecommunications – Manufacturing – Healthcare – Transport – Finance – Environmental science/engineering • Here, we’ll focus on the important common principles, rather than become entrenched in specific application areas
Some Important Questions
• What are we trying to measure? • How do we measure it? • What sorts of model can we build? • Is the model well-defined? • How can we “solve” the model? • What formalisms can we use to help define a model? • How does the model compare to the real system?
5
6
The Focus...
• We’re going to focus on discrete event systems, with continuous time and discrete states • Our models will essentially be