A general result for deriving product-form solutions of Markovian models

In this paper we provide a general method to derive product-form solutions for stochastic models. We take inspiration from the Reversed Compound Agent Theorem and we provide a different formulation using labeled automata, a generalization which encompasses a bigger class of product-form solutions, and a new proof based on the solution of the system of global balance equations. We show that our result may have practical applications in the performance evaluation of complex software and hardware architectures and can be the base for the development of new analysis tools or the extension of existing ones.