GSPNs Revisited: Simple Semantics and New Analysis Algorithms

This paper considers interactive Markov chains (IMCs), a natural generalization of transition systems and continuous-time Markov chains (CTMCs). We show how they can be used to provide a truly simple semantics of Generalized Stochastic Petri Nets (GSPNs). In fact, any GSPN. In particular, no restrictions are imposed on the concurrent/conflicting enabledness of immediate transitions. This contrasts with classical solutions for GSPNs which use weights. (A simple extension of IMCs also covers weights.) In addition, we will present novel analysis algorithms for expected time and long-run average time objectives of IMCs, i.e., GSPNs. Two case studies indicate the feasibility of these analyses and show that a classical reliability analysis for confused GSPNs may lead to significant over-estimations of the true probabilities. The key message is: nondeterminism is not a threat, treat it as is! This yields both a simple GSPN semantics and trustworthy analysis results.