Markov Chains and Dependability Theory Bruno Sericola » holypet.ru

Jul 31, 2014 · Bruno Sericola is a Senior Researcher at INRIA, France. His main research activity is in computer and communication systems performance evaluation, dependability and performability analysis of fault-tolerant systems and applied stochastic processes. Markov Chains and Dependability Theory Dependability metrics are omnipresent in every engineering field, from simple ones to more complex measures combining performance and dependability aspects of sys

Markov chains and dependability theory. [Gerardo Rubino; Bruno Sericola] -- Covers fundamental and applied results of Markov chain analysis for the evaluation of dependability metrics, for graduate students and researchers. Theory and Applications. Author: Bruno Sericola; Publisher: John Wiley & Sons ISBN: 1118731530 Category: Mathematics Page: 416 View: 5084 DOWNLOAD NOW » Markov chains are a fundamental class of stochastic processes.They are widely used to solve problems in a large number of domainssuch as operational research, computer science, communicationnetworks and manufacturing systems. Bruno Sericola is a Senior Research Scientist at Inria Rennes - Bretagne Atlantique in France. His main research activity is in performance evaluation of computer and communication systems, dependability analysis of fault-tolerant systems and stochastic models. Bruno Sericola Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational research, computer science, communication networks and manufacturing systems. Request PDF Markov Chains and Dependability Theory Dependability metrics are omnipresent in every engineering field, from simple ones through to more.

The authors first present both discrete and continuous time Markov chains before focusing on dependability measures, which necessitate the study of Markov chains on a subset of states representing different user satisfaction levels for the modelled system. Bruno SERICOLA of National Institute for Research in Computer Science and Control, Le Chesnay INRIA Read 148 publications Contact Bruno SERICOLA. Markov Chains and Dependability Theory. Analysis of a large number of Markov chains competing for transitions. International Journal of Systems Science, 453, 2014. pdf; E. Anceaume, F. Castella, R. Ludinard, B. Sericola. Markov Chains Competing for Transitions: Application to Large-Scale Distributed Systems. Methodology and Computing in Applied Probability, 152, 2013. pdf. Markov Chains and Dependability Theory. By Gerardo Rubino and Bruno Sericola. Abstract. International audienc Topics: [MATH] Mathematics [math], [INFO.INFO-PF] Computer Science [cs] /Performance [cs.PF].

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