Optimization qualitative and quantitative for dependability analysis
Author(s) -
Boucerredj Leila
Publication year - 2018
Publication title -
informatica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 34
eISSN - 1854-3871
pISSN - 0350-5596
DOI - 10.31449/inf.v42i3.1580
Subject(s) - dependability , markov chain , computer science , graph , truth table , causality (physics) , theoretical computer science , markov process , representation (politics) , table (database) , markov model , algorithm , data mining , mathematics , machine learning , statistics , physics , quantum mechanics , politics , political science , law , software engineering
Systems that are not dependable and insecure may be rejected by their users. For many systems controlled by computer, the most important system property is the dependability of the system. For this reason in this paper, we propose a complete approach for dependability analysis. The proposed approach is based on optimization qualitative and quantitative for dependability analysis, qualitative optimization is based on causality relations between the events deduced from the Truth Table Method combined with Karnaugh Table for deriving minimal feared states, q uantitative optimization is based on Reduced Markov Graph this graph is directly composed by a minimal feared state deduced from the qualitative optimization , to avoid the problem of combinatorial explosion in the number of states in the Markov graph modelling. The representation of the Markov graph will be particularly interesting to study dependability.
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