Probabilistic Model Checking of Non-Markovian Models with Concurrent Generally Distributed Timers

Abstract

In the analysis of stochastic concurrent timed models, probabilistic model checking combines qualitative identification of feasible behaviors with quantitative evaluation of their probability. If the stochastic process underlying the model is a Continuous Time Markov Chain (CTMC), the problem can be solved by leveraging on the memoryless property of exponential distributions. However, when multiple generally distributed timers can be concurrently enabled, the underlying process may become a Generalized Semi Markov Process (GSMP) for which simulation is often advocated as the only viable approach to evaluation. The method of stochastic state classes provides a means for the analysis of models belonging to this class, that relies on the derivation of multivariate joint distributions of times to fire supported over Difference Bounds Matrix (DBM) zones. Transient stochastic state classes extend the approach with an additional age clock associating each state with the distribution of the time at which it can be reached. We show how transient stochastic state classes can be used to perform bounded probabilistic model checking also for models with underlying GSMPs, and we characterize the conditions for termination of the resulting algorithm, both in exact and approximate evaluation. We also show how the number of classes enumerated to complete the analysis can be largely reduced through a look-ahead in the non-deterministic state class graph of reachable DBM zones. As notable traits, the proposed technique accepts efficient implementation based on DBM zones without requiring the split of domains in regions, and it expresses the bound in terms of a bilateral constraint on the elapsed time without requiring assumptions on the discrete number of executed transitions. Experimental results based on a preliminary implementation in the Oris tool are reported.