Efficient Modelling and Generation of Markov Automata

This paper introduces a framework for the efficient modelling and generation of Markov automata. It consists of (1) the data-rich process-algebraic language MAPA, allowing concise modelling of systems with nondeterminism, probability and Markovian timing; (2) a restricted form of the language, the MLPPE, enabling easy state space generation and parallel composition; and (3) several syntactic reduction techniques on the MLPPE format, for generating equivalent but smaller models. Technically, the framework relies on an encoding of MAPA into the existing prCRL language for probabilistic automata. First, we identify a class of transformations on prCRL that can be lifted to the Markovian realm using our encoding. Then, we employ this result to reuse prCRL's linearisation procedure to transform any MAPA specification to an equivalent MLPPE, and to lift three prCRL reduction techniques to MAPA. Additionally, we define two novel reduction techniques for MLPPEs. All our techniques treat data as well as Markovian and interactive behaviour in a fully symbolic manner, working on specifications instead of models and thus reducing state spaces prior to their construction. The framework has been implemented in our tool SCOOP, and a case study on polling systems and mutual exclusion protocols shows its practical applicability.