Performance Analysis and Simulation

Teacher:

• Michele Loreti

ESSE3 Link

• TBD

Lessons schedule:

• Tuesday, 4.00 p.m. - 6 p.m. (Room LA1)
• Thursday, 4.00 p.m. - 6 p.m. (Room LB1)

In the course various aspects of computer-aided modelling for performance evaluation of (stochastic) dynamic systems will be introduced. The emphasis is on stochastic modelling of computer systems and communication networks; however other dynamic systems such as Collective Adaptive Systems and Cyber Physical Systems will also be considered. Different tools and techniques will be introduced to predict (or monitoring) system performance at the different stages of development.

1. Modelling and Simulation
2. Operational Laws
3. Constructing and Solving Markov Processes
4. More Complex Markov Processes
5. Population Models
6. Specification languages: Stochastic process Algebras
7. Simulation Models: Introduction and Motivation
8. Random Variables and Simulation
9. Statistical Analysis of Systems
10. Property specification
11. Scalable analysis techniques
12. Monitoring and Runtime Verification

Course Slides

• Slides 01: Introduction and Basic Principles of Performance Modelling.
• Slides 02: Operational Laws.
• Slides 03: Markov Chains.
• Slides 04: Transient Analysis of CTMC. (CTMC 1,CTMC 2)
• Slides 05: Population Models.
• Slides 06: PEPA.
• Slides 07: Simulation.
• Slides 08: Statistics.
• Slides 09: Statistical Inference.
• Slides 10: Statistical Transient Analysis.
• Slides 11: Sibilla, a framework for agent simulation and analysis.

Lectures

• Lecture 1, 8/10/2019
• Lecture 2, 10/10/2019
• Lecture 3, 15/10/2019
• Lecture 4, 17/10/2019
• Lecture 5, 22/10/2019
• Lecture 6, 24/10/2019
• Lecture 7, 29/10/2019
• Lecture 8, 31/10/2019
• Lecture 9, 5/11/2019
• Lecture 10, 7/11/2019
• Lecture 11, 12/11/2019
• Lecture 12, 14/11/2019
• Lecture 13, 19/11/2019
• Lecture 14, 21/11/2019
• Lecture 15, 26/11/2019
• Lecture 16, 28/11/2019
• Lecture 17, 3/12/2019
• Lecture 18, 5/12/2019
• Lecture 19, 10/12/2019
• Lecture 20, 12/12/2019
• Lecture 21, 17/12/2019

Exam Dates

• The date is defined with the teacher.

Exam rules: Each student selects one of the following topics and produces a short report (between 5 and 10 pages) that will be discussed at the exam:

• Use either Sibilla or one of the tools listed above to model a scenario. Examples of possible scenarios are:
  • Epidemic Model
  • Diffusion Model
  • Predator-Prey Model
  • Asynchronous Leader Election
  • Crowds protocol
  • Stable matching
  • …
• Study one of the following topics:
  • Statistical Model Checking (study the methods that was not considered in the lectures)
  • Robust Online Monitoring of Signal Temporal Logic
  • System design of stochastic models using robustness of temporal properties
  • Smoothed model checking (A technique for parameter optimisation)
  • Continuous approximation of stochastic models
• A student can suggest a topic of her/his interest.