Buongiorno,
allego l'annuncio del seminario per la settimana prossima, che sarà
tenuto da Giulio Masetti (ISTI-CNR).
Colgo l'occasione per ricordarvi l'appuntamento di domani, per il
seminario di Stefano Massei, alle 10:00 in Aula Seminari Ovest,
Informatica.
-- Leonardo Robol.
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Speaker: Giulio Masetti
Affiliation: ISTI-CNR
Time: Thursday, 13 December 2018, h. 10:00
Place: Aula Seminari, Dipartimento di Matematica
Title: Computing dependability-oriented measures on Markov chains by
means of matrix functions
In the context of computing and communication system, dependability is
defined as the ability to deliver service that can justifiably be trusted.
Dependability is then an umbrella term that encompasses several
attributes, such as: availability, reliability, safety, integrity,
maintainability, and confidentiality. Design and analysis of models
capable to express functional specifications and behavioural aspects is
an important part of the system dependability justification process.
In particular, Markov chains reflect important characteristics of
computing systems such as discrete states and memoryless property,
and then are often employed to model complex systems.
Starting from the model Markovian process, other processes can be
defined to express gains or losses according to which state the Markov
process is in, and then all the mentioned dependability attributes
can be evaluated as measures on these processes.
More and more complex systems are designed, and then
new techniques are required to tackle the evaluation of
dependability-oriented measures on large models.
In a world where systems comprise hundreds or thousands of
interconnected components, justifying dependability is an ever
increasing challenge.
The main contribution presented in this talk is, focusing on Continuous
Time Markov Chains, recasting dependability-oriented measures as the
evaluation of a bilinear form where the matrix is indeed a matrix
function of the infinitesimal generator matrix characterizing the
Markovian stochastic process. In particular, chains with absorbing
states, relevant to evaluate system reliability and connected
attributes, represent one of the main challenges the modeling community
has to deal with, and -- we think -- one of the cases where applying
results from the matrix functions body of knowledge can have the greater
impact.
