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Computer-aided simulations lab
01DTHSM
A.A. 2023/24
Course Language
Inglese
Degree programme(s)
Master of science-level of the Bologna process in Data Science And Engineering - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 20 |
| Esercitazioni in aula | 60 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Leonardi Emilio | Professore Ordinario | IINF-03/A | 15 | 33 | 0 | 0 | 3 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/03 | 8 | C - Affini o integrative | Attività formative affini o integrative |
|---|
2023/24
The course provides to students the basic knowledge on performance evaluation of dynamic systems via computer simulations, and the skills of using some fundamental methodologies for the assessment, design and understanding of data processing systems. Two complementary approaches are discussed to study complex discrete systems such as computer networks and data processing systems. On the one hand, the course introduces analytical modelling based on theory stochastic processes and queuing theory. On the other hand, students will be introduced to simulation techniques, which can be applied to study the dynamic evolution of a system as a function of time. Applications of these tools to case studies of practical interest are presented and developed in the activities in the lab.
Computer simulation is a versatile methodology to study the evolution of complex dynamic systems in many scenarios, e.g., data stream processing, big data storage, social networks, epidemics, computer networks, production systems. Typically, simulations allow to model complex interactions between entities and to evaluate the output metrics under generic settings, for which alternative analytical approaches cannot be directly applied.
The course will consider many practical scenarios and for each of them the following steps will be considered
- description and discussion of the considered scenario
- design of the abstract simulation model
- analytical modelling under simplified assumptions, used as validation for the simulation
- design of the event-driven simulation model
- coding, validation and testing of the simulation model
The adopted teaching methodology is based on a hands-on approach with in-class lab design and coding sessions. Prompt feedback will be provided to lab activities through a peer-grading system.
- Knowledge of the main elements of a simulator
- Ability to evaluate the performance of a dynamic discrete system through simulation
- Ability to understand the fundamental behaviour of a dynamic discrete system in terms of its stability, performance characteristics and limits, bottlenecks
- Ability to model flows of vehicles, data, people, as well as the interactions among elements of complex dynamic systems
- Ability to compare in a quantitative way two dynamic discrete systems
- Knowledge of the main elements of a simulator
- Ability to evaluate the performance of a dynamic discrete system through simulation
- Ability to understand the fundamental behaviour of a dynamic discrete system in terms of its stability, performance characteristics and limits, bottlenecks
- Ability to model flows of vehicles, data, people, as well as the interactions among elements of complex dynamic systems
- Ability to compare in a quantitative way two dynamic discrete systems
Basic knowledge of probability theory. Basic programming skills (python).
Basic knowledge of probability theory. Basic programming skills (python).
Theory of simulation (3CFU): - Basic concepts of performance evaluation - Discrete-event
Theory of simulation (3CFU):
- Basic concepts of performance evaluation
- Discrete-event and process simulation
- Fitting empirical distributions
- Simulating traffic sources
- Analysis of the output
– Identification of transients
- Analysis of transient behaviors
Application to epidemic systems (1CFU)
- Introduction to Galton-Watson processes
- Asymptotic behavior and phase transition
- Evaluation of the extinction probability
- Generalizations (timed process) and epidemic process on graphs.
Queueing systems (0.8 CFU)
- Basic concepts of queuing systems
- Queuing systems in isolation
- Load and system stability
- Little's law
Application to approximated data structures (2CFU)
- bins and balls models
- hash functions and fingerprinting
- hash tables and Bloom filters
Application to other scenarios (1.2 CFU)
Theory of simulation (2CFU):
- Basic concepts of performance evaluation
- Discrete-event and process simulation
- Fitting empirical distributions
- Input stochastic process
- Analysis of the output and confidence intervals
- Transients detection
Application to epidemic systems (2CFU)
- Introduction to Galton-Watson processes
- Asymptotic behavior and phase transition
- Evaluation of the extinction probability
- Generalizations (timed process) and epidemic process on graphs.
Application to data structures for big data (2CFU)
- bins and balls models
- hash functions and fingerprinting
- hash tables and Bloom filters
Probabilistic data structures for data stream processing (1CFU)
- Sketches, hyperloglog (HLL) counters
Queueing systems (1 CFU)
- Basic concepts of queuing systems
- Queuing systems in isolation
- Load and system stability
- Little's law
- 50 h lectures (L): besides traditional theoretical classes, several problems will be proposed, discussed and solved
- 30 h lab (EL): examples of simulators will be developed in class
- 50 h lectures (L): besides traditional theoretical classes, several problems will be proposed, discussed and solved
- 30 h lab (EL): examples of simulators will be developed in class
The teaching material will be provided by the teachers on the web portal. A useful textbook is:
Leemis, Lawrence M., and Stephen K. Park. Discrete-event simulation: A first course. Prentice-Hall, Inc., 2005.
The teaching material will be provided by the teachers on the web portal. A useful textbook is:
Leemis, Lawrence M., and Stephen K. Park. Discrete-event simulation: A first course. Prentice-Hall, Inc., 2005.
Slides; Video lezioni dell’anno corrente;
Lecture slides; Video lectures (current year);
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale individuale;
Exam: Compulsory oral exam; Individual project;
...
The evaluation of the exam will consist of three parts. 1) A report on the lab activities (30% final grade) 2) Bonus points for live lab activities (20%) 3) An oral exam (50% final grade).
The oral exam consists of 2/3 questions about every argument considered in the course.
The goal of the exam is to evaluate knowledge and abilities acquired by the student.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Individual project;
The evaluation of the exam will consist of three parts. 1) A report on the lab activities
2) Bonus points for live lab activities 3) An oral exam. The weights of three parts will be specified at the beginning of the course.
The oral exam consists of questions about every topic considered in the course as well as a discussion on lab reports.
The goal of the exam is to evaluate knowledge and abilities acquired by the student.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.