Markov automata (MA) constitute an expressive continuous-time compositional modelling formalism. They appear as semantic backbones for engineering frameworks including dynamic fault trees, Generalised Stochastic Petri Nets, and AADL. Their expressive power has thus far precluded them from effective analysis by probabilistic (and statistical) model checkers, stochastic game solvers, or analysis tools for Petri net-like formalisms. This paper presents the foundations and underlying algorithms for efficient MA modelling, reduction using static analysis, and most importantly, quantitative analysis. We also discuss implementation pragmatics of supporting tools and present several case studies demonstrating feasibility and usability of MA in practice.
|Place of Publication||Ithaca, NY, USA|
|Number of pages||27|
|Publication status||Published - 30 May 2013|
- EC Grant Agreement nr.: FP7/2007-2013
- EC Grant Agreement nr.: FP7/318490
- Continuous time
- Process Algebra
- Markov Automata
- EC Grant Agreement nr.: FP7/257005
- Quantitative analysis
- EC Grant Agreement nr.: FP7/295261
Guck, D., Hatefi, H., Hermanns, H., Katoen, J. P., & Timmer, M.
(2013). Modelling, Reduction and Analysis of Markov Automata (extended version)
. Ithaca, NY, USA: Cornell University.