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# Hierarchical Reachability Graph Generation for Petri Nets

Hierarchical Reachability Graph Generation for Petri Nets

## 306 BUCHHOLZ AND

306 BUCHHOLZ AND KEMPERThe Kronecker representation requires overall 195 transitions to represent the completereachability graph with 622 transitions. Of course, this comparison does not consider overheadof storing different matrices in the Kronecker representation. However, the overheaddepends on the number of transitions in ∪ST i and on the number of LNs. Both quantities arenegligible compared to the number of markings if we consider large nets. The hierarchicallygenerated reachability set RS h includes 270 markings, which means that 16 markings areunreachable. We consider this point in the subsequent section.7. Hierarchical analysisA Kronecker representation has advantages for analysis algorithms. In this section wedescribe how to recognize RS(PN) in a Kronecker representation by a search algorithm,Depth-First-Search (DFS) or Breadth-First-Search (BFS). This serves two purposes: a)knowledge of the exact RS(PN) is important for state-based analysis and b) many statebasedanalysis algorithms are search algorithms as well, e.g., model-checking algorithmsfor computational tree logic (CTL) in [20] are search algorithms and require knowledge ofRS(PN). Reachability analysis for Kronecker representations profits from the fact that thenumbering of markings in RS H (PN s ) is a perfect hash function for markings in RS(PN).This was first exploited for the efficient reachability analysis of SGSPNs, a class of generalizedstochastic PNs consisting of components synchronized via transitions, in the work ofKemper [32]. We can use a similar approach here, but do not necessarily rely on it; see, forexample, [11, 18, 19] for alternatives. For a marking M x of the HN, let n(x) = ∏ Ji=1 n i(x i )be the number of markings in RS h . Let r[x] be a Boolean vector of length n(x) that isused to store results of the reachability analysis. Thus r[x H ](x L ) = 1 indicates that marking(x H , x L ) ∈ RS(PN), and r[x H ](x L ) = 0 indicates after termination that (x H , x L ) ∉ RS(PN).Formally, we use here one Boolean vector per HN marking, but it is obviously possible tostore all these vectors consecutively in a single Boolean vector of appropriate length. Inaddition to the vectors r[x] and the different matrices introduced in the previous section,reachability analysis also requires a set U to store unexplored markings, similar to the setU used in generate RS in Section 2. However, in the current setting, U only has to storeinteger pairs instead of complete marking vectors.Let (x 0H , x 0L ) be the number of the initial marking; then r[x 0H ](x 0L ) is initialized with 1,and all remaining vector components are zero. Additionally, U is initialized with (x 0H , x 0L ).Then the following algorithm is used to determine reachable markings.generate structured RS (PN)while (U ≠ ∅) doremove (x H , x L ) from U;for j = 1toJdofor all y j with Q jl (x j , y j ) = 1 doy L = x L + (y j − x j ) · l j (x H )if r[x H ](y L ) = 0 thenr[x H ](y L ) = 1;U = U ∪ (x H , y L );// compute successor in subnet j

HIERARCHICAL REACHABILITY GRAPH GENERATION FOR PETRI NETS 307endfor all y H with Q H (x H , y H ) ≠ ∅ dofor all t ∈ Q H (x H , y H ) doy L = x L ;for all j with t ∈ T j doif y j L with Q j [jt xH , y j Hy L = y L + ( y j L − x j L)· l j (y H );elsey L =−1 and break;if y L ≥ 0 thenif r[x H ](y L ) = 0 thenr[x H ](y L ) = 1;U = U ∪ (x H , y L );// compute successor in subnet HN](xjL , y j L)= 1 exists then (*)Note that the order in which elements are inserted and removed from U makes thisalgorithm perform DFS or BFS. In the step indicated by (∗), the algorithm exploits the factthat firing of transition t always yields a unique successor marking. Therefore, each rowof a matrix Q i t can include at most one element. The approach can be easily extended forPNs in which different successor markings are possible. This situation occurs in stochasticnets in which probabilistic output bags for transitions are allowed. Since the algorithmcomputes all successor markings of reachable markings, it is straightforward to prove thatgenerate structured RS generates RS(PN) and terminates when RS(PN) isfinite, as is thecase here, since RS H (PN s ) is assumed to be finite.The remaining point is the comparison of generate structured RS (from Section 2) andgenerate RS. As before, we assume that the reachability set contains n markings, and thaton the average, d transitions are possible in each marking. The theoretical time complexityof generate RS is O(nd log 2 n) if insert and member functions on RS use log 2 n operations.The complexity of generate structured RS is in O(nd), since the Boolean vectors allowus to test in O(1) whether a marking has been reached before. Additionally, the constantsbehind the asymptotic complexity are much lower for generate structured RS. The reason isthat all operations are performed with simple integer operations, while several operations ofgenerate RS are time-consuming. For example, if a new marking M is found in generate RS,a data structure to hold M has to be allocated and inserted into the data structure storingthe already generated markings. Since this data structure is usually a tree, pointers haveto be modified. In generate structured RS the same operation only requires the setting ofa bit in vector r. Thus, we can usually expect an improvement of run times of about twoorders of magnitude for large reachability sets. However, to apply generate structured RS,PN has to be decomposed, and then the reachability sets and matrices for the subnets haveto be generated. The complexity of both problems is, for large nets, much lower than thatof reachability analysis. This can also be seen in the example presented below.In addition to time complexity, we must also compare space complexity. Of course, thedifference in memory requirements depends on the concrete example. However, if the nethas been decomposed into LNs with roughly identically sized reachability sets, and the sizesof RS H (PN s ) and RS(PN) do not differ too much (i.e., not by several orders of magnitude),

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