Enhancing Multiformalism Models with Rewrite Engines

This paper explores the enhancement of multiformalism modeling frameworks by integrating rewrite engines, with a focus on SIMTHESys, a framework designed for combining and solving heterogeneous formalisms. Multiformalism modeling enables the use of the most suitable formalism for each subsystem while maintaining an overall coherent representation of the system. As model complexity and the need for dynamic adaptability increase, static modeling structures become inadequate. To address this, we extend SIMTHESys with capabilities for dynamic transformations and late binding by incorporating a rewriting system, specifically leveraging the Maude engine. This integration allows models to evolve during their solution processes, supporting more flexible, efficient, and modular analyses. We present a case study involving the dynamic management of a server system modeled through stochastic Petri nets and multiclass queuing networks, demonstrating the feasibility and advantages of the proposed approach. Experimental results highlight how rewriting enhances model expressiveness and efficiency in complex system evaluations.