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Extending stochastic symmetric nets with graph transformations for the formal modeling and analysis of evolvable systems
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DOI：10.1007/s10586-026-06434-w.png)
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Abstract
En 中文
Dynamic-structure systems, those whose architecture evolves at runtime, are increasingly prevalent in domains such as adaptive networks, distributed control, and reconfigurable manufacturing systems. Modeling and analyzing such systems pose several significant challenges, particularly when both stochastic behavior and structural reconfiguration must be captured. While stochastic symmetric nets (S2Ns) offer a powerful formalism for compactly modeling concurrent systems with probabilistic timing, their static structure limits their applicability to systems with evolving topologies and structures. This paper proposes a new formalism called reconfigurable stochastic symmetric nets (RecS2Ns), an extension of S2Ns that incorporates rule-based dynamic reconfiguration grounded in graph transformation theory. RecS2Ns preserve the expressiveness and verifiability strengths there is a mapping o of S2Ns, such as symmetry, stochastic semantics, and scalability, while enabling the formal specification of structural changes over time. We introduce a formal syntax and semantics for reconfiguration rules and define an unfolding algorithm that transforms a RecS2N into an equivalent static S2N. This enables the reuse of existing S2N-based tools for verification and performance analysis. Moreover, we formally prove the equivalence between the original dynamic-structure models and their static unfolded models. The effectiveness of the proposed formalism in the design of dynamic-structure systems is demonstrated through a reconfigurable manufacturing system case study.
Keywords:
High-level Petri nets
Stochastic symmetric nets
Reconfigurable systems
Dynamic system design
Rule-based transformation
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