Obiettivi Formativi

Acquisire le nozioni su: - Analisi dei sistemi esistenti valutandone il comportamento dal punto di vista delle prestazioni e della affidabilita'. - Determinazione delle componenti di un sistema per ottimizzarne il comportamento. - Definizioni di piani di test per la valutazione di metriche di comportamento di un sistema.

Learning Goals

To learn how to: - Analyse existing systems, evaluating its behaviour in terms of performance and reliability. - Design number and nature of components of a system in order to optimize system behaviour, - Define a test plan for assessment of specific requirements of a system.

Metodi didattici

- Lezioni frontali. - Esercitazioni in laboratorio.

Teaching Methods

- Lectures. - Practical exercises Laboratory.

Prerequisiti

Nozioni di base su: - Teoria delle probabilità e processi stocastici - linguaggi di programmazione

Prerequisites

Basics on: - probability theory and stochastic processes - programming languages

Verifiche dell'apprendimento

- Sviluppo di un progetto. - Discussione sugli argomenti in programma.

Programma del Corso

Performance evaluation: - Basic concepts - Basics on probability theory. Random variables. Probability distribution functions. Operation with random variables. The exponential distributed random variable. - Reliability: definition, failure rate, bath tube curve. Weibull distribution. - Reliability Block Diagram: systems in series, systems in parallel. - Redundancy: component, system, standby, N/K system. - Availability. Availability computation. Mean Time To Failure (MTTF). Mean Time To Repair (MTTR). Availability of systems in series. Availability of parallel system. - Modeling with stochastic Markov chain. - Examples: birth-death processes, availability with different repair policies. - Performability. Reward processes. - Petri nets: formal definition, marking process. Stochastic Petri nets. Generalized stochastic Petri nets. Solution techniques. Stochastic reward nets. Performance and reliability evaluation with Petri nets. - Discrete event simulation. Structure of a discrete event simulator. Random-number generators. Simulation models. Output analysis for a single model: measures of performance and their estimation. Confidence-interval estimation. Output analysis for terminating simulation. Output analysis for steady-state simulation. - Test plan definition. Measurements campaign definition and verification.

Course Syllabus

Performance evaluation: - Basic concepts - Basics on probability theory. Random variables. Probability distribution functions. Operation with random variables. The exponential distributed random variable. - Reliability: definition, failure rate, bath tube curve. Weibull distribution. - Reliability Block Diagram: systems in series, systems in parallel. - Redundancy: component, system, standby, N/K system. - Availability. Availability computation. Mean Time To Failure (MTTF). Mean Time To Repair (MTTR). Availability of systems in series. Availability of parallel system. - Modeling with stochastic Markov chain. - Examples: birth-death processes, availability with different repair policies. - Performability. Reward processes. - Petri nets: formal definition, marking process. Stochastic Petri nets. Generalized stochastic Petri nets. Solution techniques. Stochastic reward nets. Performance and reliability evaluation with Petri nets. - Discrete event simulation. Structure of a discrete event simulator. Random-number generators. Simulation models. Output analysis for a single model: measures of performance and their estimation. Confidence-interval estimation. Output analysis for terminating simulation. Output analysis for steady-state simulation. - Test plan definition. Measurements campaign definition and verification.