Obiettivi Formativi

Il corso mira a fornire le conoscenze di base per poter analizzare i comportamenti di un generico sistema, con particolare riferimento ai sistemi informatici, facendo uso di tecniche di rappresentazione formale. A tal fine vengono forniti i concetti di base relativi a modellazione stocastica, affidabilità e disponibilità, acquisendo le nozioni su: - tecniche di analisi di un sistema esistente per valutarne il comportamento dal punto di vista delle prestazioni e della affidabilità; - determinazione delle componenti di un sistema per ottimizzarne i parametri comportamentali di interesse; - definizione di piani di test per la valutazione di metriche di comportamento di un sistema. Facendo largo uso in laboratorio di software di analisi assistita al calcolatore si mira inoltre a far maturare la capacità di sintetizzare un modello di comportamento di un sistema informatico e derivare le informazioni quantitative sulle metriche di interesse ad esso relative.

Learning Goals

The course aims to provide the basic knowledge to be able to analyze the behavior of a generic system, with particular reference to computer systems, making use of formal representation techniques. To this goal, the basic on to stochastic modeling, reliability and availability are provided, for acquiring the concepts on: - analysis techniques of an existing system to evaluate its behavior in terms of performance and reliability; - identification of the main components of a system to modify for optimizing the behavioral parameters of interest; - definition of test plans for the evaluation of system behavior metrics. Making extensive use of computer-aided analysis software in the laboratory, the aim is also to develop the ability to synthesize a computer system's behavior model and derive quantitative information on the relevant metrics of interest.

Metodi didattici

- Lezioni frontali facendo uso di materiale didattico in formato elettronico reso disponibile dal docente tramite il sito di e-learning dell’Università. - Esercitazioni in laboratorio con uso di software tool di modellazione e realizzaziono di casi di studio di valutazione delle prestazioni ed affidabilità di sistemi informatici e di telecomunicazione.

Teaching Methods

- Lectures, with use of educational material in electronic format made available by the teacher through the University's e-learning site. - Laboratory exercises with the use of software modeling tools and the study of case studies for evaluating the performance and reliability of IT and telecommunications systems.

Prerequisiti

Per una utile comprensione degli argomenti, sono richieste conoscenze di base su: - teoria delle probabilità e processi stocastici - architetture hardware e software dei sistemi informatici - linguaggi di programmazione

Prerequisites

For a useful understanding of the topics, it is needed basic knowledge on: - probability theory and stochastic processes - computer systems hardware and software architectures - programming languages

Verifiche dell'apprendimento

La verifica dell’apprendimento in itinere è demandata alle frequenti esercitazioni svolte durante lo svolgimento delle lezioni. Periodicamente, viene richiesto l’applicazione delle metodologie presentate per risolvere un problema che viene posto alla classe e la cui soluzione è proposta dagli studenti e discussa con la classe sotto la guida del docente. La prova finale consiste nello sviluppo di un semplice progetto da svilupparsi secondo le metodologie presentate e sperimentate durante le verifiche periodiche svolte in classe, con una breve discussione degli aspetti teorici ad esso collegati, durante cui si dovrà adeguatamente supportare e giustificare le scelte di progetto fatte.

Assessment

The verification of the ongoing learning is entrusted to the frequent exercises carried out during lectures. Periodically, the application of the presented methodologies is required to solve a problem that is posed to the class and whose solution is proposed by students and discussed with the class under the guidance of the teacher. Final exam consists in developing a simple project according to the methodologies presented and tested during the periodic verifications carried out in the classroom, together with a brief discussion of the theoretical aspects connected to it. Sirtudents will also have to adequately support and justify the project choices.

Programma del Corso

- Basic concepts on Performance Evaluation - 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. Laboratory activities: use of software tools for the design and numerical analysis of Reliability Block Diagrams, and stochastic Petri nets.

Course Syllabus

- Basic concepts on Performance Evaluation - 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. Laboratory activities: use of software tools for the design and numerical analysis of Reliability Block Diagrams, and stochastic Petri nets.