Stochastic Methods for Analysis of Complex Hardware-Software Systems
In this paper we consider Markov analysis of models of complex software and hardware systems. A Markov analysis tool can be used during verification processes of models of avionics systems. In the introduction we enumerate main advantages and disadvantages of Markov analysis. For example, with Markov analysis, unlike other approaches, such as fault tree analysis and dependency diagram analysis, it is possible to analyze models of systems that are able to recovery. The main drawback of this approach is an exponential growth of models size with number of components in analyzed system. It makes Markov analysis barely used in practice. The other important problem is to develop a new algorithm for translating a model of a system to a model suitable for Markov analysis (Markov chain), since the existing solutions have significant limitations on the architecture of analyzed systems. Next we give a brief description of the context – AADL modeling language with Error Model Annex library, MASIW framework, and also give an explanation of Markov analysis method. In a main section we suggest an algorithm for translating a system model into a Markov chain, partially solving the problem of exponential growth of Markov chain. Then follows a description of further steps, and some heuristics that allow to extremely reduce running time of the algorithm. In this paper we also consider other Markov analysis tools and their features. As a result, we suggest a Markov analysis tool that can be effectively use in practice.
Proceedings of the Institute for System Programming, vol. 29, issue 4, 2017, pp. 191-202
ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).
DOI: 10.15514/ISPRAS-2017-29(4)-12Full text of the paper in pdf Back to the contents of the volume