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B018966 - QUANTITATIVE ANALYSIS OF SYSTEMS
Main information
Course Content
Suggested readings
Learning Objectives
Teaching Methods
Type of Assessment
Course program
Academic Year 2012-13
Coorte 2012 - Second Cycle Degree in COMPUTER SCIENCE
Course year
First year - Second Semester
Belonging Department
Mathematics and Computer Science "Ulisse Dini" (DIMAI)
Course Type
Single education field course
Scientific Area
INF/01 - INFORMATICS
Credits
9
Teaching Hours
72
Teaching Term
04/03/2013 ⇒ 14/06/2013
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Course Content
Concepts of reliability and performance of computer systems and validation. Rules for model construction and validation. Probabilities, Combinatorial methods and related formalisms. Formalism for state space modeling and related solution techniques. Automatic tools supporting model based evaluation. Foundations of measurement theory and their application. Testing and supportino tools. Experiment organization. Dependability benchmarking.
Suggested readings (Search our library's catalogue)
- A. Bondavalli, F. Brancati, A. Cecccarelli, S. Chiaradonna,
D. Cotroneo, P. Lollini, L. Montecchi, R. Natella, and
M. Vadursi.
L'Analisi Quantitativa dei Sistemi Critici.
I Edizione. Esculapio Progetto Leonardo, Bologna, 2011.
- K. S. Trivedi.
Probability and Statistics with Reliability, Queueing and
Computer Science Applications.
John Wiley & Sons, New York, second edition, 2002.
D. Cotroneo, P. Lollini, L. Montecchi, R. Natella, and
M. Vadursi.
L'Analisi Quantitativa dei Sistemi Critici.
I Edizione. Esculapio Progetto Leonardo, Bologna, 2011.
- K. S. Trivedi.
Probability and Statistics with Reliability, Queueing and
Computer Science Applications.
John Wiley & Sons, New York, second edition, 2002.
Learning Objectives
Provide students with the necessary notions to comprehend the quantitative analysis and the Quality of Service metrics of computer systems. Particular emphasis is devoted to the reliability and performance analysis and evaluation. As a result, the students will learn the principles of computer systems quantitative analysis and some model based evaluation techniques and of experimental evaluation. In addition the students will learn both to define and solve stochastic models with the support of automatic tools such as Mobius and DEEM, and to design and run experimental campaigns on real systems or prototypes.
Teaching Methods
Lectures using transparencies, pc description of the software instruments, examples/practices with computers and presentations
Type of Assessment
Solution of a small project and oral interview, conditional to the successful evaluation of a written part.
Course program
Concepts and definition of systems performance and reliability. Concept of validation and rules for building and validating models.
Recall of probability theory and random variables, both discrete and continuous. Common distributions, Average, Variance…
Combinatorial modeling formalisms and related solutions techniques: Reliability Block Diagrams (RBD), Fault Trees (FT) and Reliability Graphs (RG).
State Space modeling formalisms and related solutions techniques: Discrete Time and Continuous Time Markov Processes (DTMC, CTMC); basic Petri Nets (P/T), priority based and stochastic (Stochastic Petri Nets, Generalized Stochastic Petri Nets, Deterministic Stochastic Petri Nets, Stochastic Activity Networks).
Transient and steady state solutions. Definition of relevant indicators and of performance measures. Examples of application of the proposed modeling formalisms.
Automatic instruments supporting model based evaluation: Mobius and DEEM.
Foundations of measurement theory and their application. System testing: functional testing, robustness testing, fault injection.
Organization and planning of experiments.
Benchmarks: performance benchmarking and dependability benchmarking.
Tools to support testing: Nekostat.
Application of the proposed experimental methods.
Recall of probability theory and random variables, both discrete and continuous. Common distributions, Average, Variance…
Combinatorial modeling formalisms and related solutions techniques: Reliability Block Diagrams (RBD), Fault Trees (FT) and Reliability Graphs (RG).
State Space modeling formalisms and related solutions techniques: Discrete Time and Continuous Time Markov Processes (DTMC, CTMC); basic Petri Nets (P/T), priority based and stochastic (Stochastic Petri Nets, Generalized Stochastic Petri Nets, Deterministic Stochastic Petri Nets, Stochastic Activity Networks).
Transient and steady state solutions. Definition of relevant indicators and of performance measures. Examples of application of the proposed modeling formalisms.
Automatic instruments supporting model based evaluation: Mobius and DEEM.
Foundations of measurement theory and their application. System testing: functional testing, robustness testing, fault injection.
Organization and planning of experiments.
Benchmarks: performance benchmarking and dependability benchmarking.
Tools to support testing: Nekostat.
Application of the proposed experimental methods.