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Hierarchical Reachability Graph Generation for Petri Nets

Hierarchical Reachability Graph Generation for Petri Nets


312 BUCHHOLZ AND KEMPERFigure 8.Number of aggregated places, number of non-trivial regions.requirements show that much larger systems can be handled with the approach. We havealso analyzed an open version of the production cell for which other tools were not able togenerate RS (see [26]). For this version, our method needs about 23 seconds real time togenerate RS with 2,776,936 markings and RG with 13,152,132 arcs.As already noticed in [27], computation of a generating set of semi-positive P-invariantsis difficult for this net. Our approach is closely related to invariant computation; if wecompute an extended net for a sequence that covers all transitions T , we obtain a generatingset of P-invariants as well. However, this extreme is not suitable, and we consider only asubset of transitions, in order to retain some activity in the HN. From a pragmatic pointof view, the approach allows us to consider those transitions that can be handled withacceptable computational costs and stop the derivation of a hierarchy if it becomes tooexpensive. Figure 8 clearly indicates that a careful selection of transitions can avoid highcomputational costs. However, there is a sharp increase after 108 steps, and the hierarchyderivation stops after 113 steps. For a P-invariant computation, 202 steps are necessary;hence, Figure 8 also illustrates the difficulties of invariant computation observed in [27].According to the results in Table 1, the number of regions and a limit for the number ofaggregated places give suitable parameters to stop the automatic hierarchy generation whenit makes sense to.9. ConclusionsWe have proposed a new approach for the efficient generation and compact representationof reachability sets and graphs of large PNs. In contrast to approaches based on reductionor symmetries, as in [15, 31, 39, 45, 46], the technique yields the full RS and RG. It

HIERARCHICAL REACHABILITY GRAPH GENERATION FOR PETRI NETS 313can be applied to general nets without any user-defined structure, such as is required in[4, 11, 18, 19, 32]. The structuring of the PN into asynchronously interacting regions isdone automatically by an algorithm that uses a basic step related to invariant computationto make a transition internal to a region. The algorithm considers a sequence of distincttransitions that can be arbitrary in principle. For our implementation we use some heuristicrules in order to structure a net into regions of approximately the same size. The algorithmstops once a user-given number of regions has been obtained. The idea is to divide thecomplexity equally well among HN and LNs, which is most efficient if neither HN norany LN is too complex or too simple. For nets covered by P-invariants, termination isguaranteed; however, we can not ensure termination for general PNs. The problem is thatreachability sets of some part, HN or a LN, can become unbounded, even if the reachabilityset of the complete net is bounded. This problem can not occur for nets that are covered byP-invariants.The non-trivial example considered in this paper illustrates our experience with the algorithmexercised on a set of examples. The new approach allows the time- and space-efficientgeneration and representation of huge reachability sets and graphs. This is, of course, a steptowards the analysis of complex PNs. The algorithm is implemented and integrated into atoolbox around a common file format for Petri nets [2, 10]. This toolbox includes algorithmsfor LTL and CTL model-checking that use the compact matrix representation. Experiencesshow that this realization of model-checking algorithms allows the efficient analysis of verylarge reachability sets. Additionally, our approach naturally extends to stochastic models[9]; the resulting Kronecker representation can be used for the efficient analysis of SPNsusing numerical analysis techniques. For an overview of these techniques, we refer to [7].The present approach does not use behavior-preserving reductions; however, once a hierarchicalstructure is obtained, we foresee that it will be useful to employ behavior-preservingreduction according to some bisimulation type equivalences in order to analyze larger andlarger models.Notes1. A slightly extended version catches infinite RS; see coverability graph construction in the PN literature.2. We do not consider here the OBDD techniques that have different characteristics, complexities, and limitations.3. Minimal regions coincide with the equivalence relation of the conflict relation [43].4. We thank J. Spranger for translating the model into the APNN format [2] used in our implementation.References1. S. Allmaier, M. Kowarschik, and G. Horton, “State space construction and steady state solution of GSPNson a shared-memory multiprocessor,” in Proc. 7th Int. Workshop on Petri Nets and Performance Models(PNPM’97), IEEE CS Press, 1997, pp. 112–121.2. F. Bause, P. Kemper, and P. Kritzinger, “Abstract Petri net notation,” Petri Net Newsletter, Vol. 49, pp. 9–27,1995.3. G. Berthelot, “Transformation and decomposition of nets,” in G. Rozenberg (Ed.), Advances in Petri Nets 85,Springer, 1986. LNCS, Vol. 254.4. P. Buchholz, “Hierarchical high level Petri nets for complex system analysis,” in R. Valette (Ed.), Applicationand Theory of Petri Nets, Springer, 1994, pp. 119–138. LNCS, Vol. 815.

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