Symbolic structural techniques improving the analysis of Stochastic Symmetric Nets

Stochastic symmetric nets (SSN) represent a high-level Petri net formalism characterized by succinct annotations that encapsulate behavioral symmetries. These symmetries are utilized to enhance the efficiency of analysis, such as the construction of a symbolic reachability graph from which a lumped Markov chain is derived and the execution of symbolic discrete-event simulations. In the last two decades, powerful structural analysis techniques have also been developed for SSN. This article explores how these techniques can improve some performance analysis techniques, highlighting potential uses and recent progress.