Gianfranco Ciardo

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We present a decomposition approach for the solution of large stochastic reward nets (SRNs) based on the concept of near-independence. The overall model consists of a set of submodels whose interactions are described by an import graph. Each node of the graph corresponds to a parametric SRN submodel and an arc from submodel A to submodel B corresponds to a(More)
Stochastic Petri nets (SPNs) with generally distributed firing times can model a large class of systems, but simulation is the only feasible approach for their solution. We explore a hierarchy of SPN classes where modeling power is reduced in exchange for an increasingly efficient solution. Generalized stochastic Petri nets (GSPNs), deterministic and(More)
Kronecker-based approaches have been proposed for the solution of structured GSPNs with extremely large state spaces. Representing the transition rate matrix using Kronecker sums and products of smaller matrices virtually eliminates its storage requirements, but introduces various sources of overhead. We show how, by using a new data structure which we call(More)
This paper presents a novel algorithm for generating state spaces of asynchronous systems using Multi-valued Decision Diagrams. In contrast to related work, the next-state function of a system is not encoded as a single Boolean function, but as cross-products of integer functions. This permits the application of various iteration strategies to build a(More)
We present a new technique for the generation and storage of the reachability set of a Petri net. Our approach is inspired by previous work on Binary and Multi-valued Decision Diagrams but exploits a concept of locality for the effect of a transition’s firing to vastly improve algorithmic performance. The result is a data structure and a set of manipulation(More)
We present new algorithms for the solution of large structured Markov models whose infinitesimal generator can be expressed as a Kronecker expression of sparse matrices. We then compare them with the shuffle-based method commonly used in this context and show how our new algorithms can be advantageous in dealing with very sparse matrices and in supporting(More)
We discuss how to describe the Markov chain underlying a generalized stochastic Petri net using Kronecker operators on smaller matrices. We extend previous approaches by allowing both an extensive type of marking-dependent behavior for the transitions and the presence of immediate synchronizations. The derivation of the results is thoroughly formalized,(More)
SMART (http://www.cs.wm.edu/ ciardo/SMART/) is a software package integrating logic and stochastic modeling formalisms into a single environment. Models expressed in different formalisms can be combined in the same study. To study logical behavior, both explicit and symbolic state-space generation techniques, as well as CTL modelchecking algorithms, are(More)