Approximate performance models of polling systems using Stochastic Petri nets
IEEE INFOCOM '92 Proceedings of the eleventh annual joint conference of the IEEE computer and communications societies on One world through communications (Vol. 3)
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IEEE Transactions on Software Engineering - Special issue: best papers of the sixth international workshop on Petri nets and performance models (PNPM'95)
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IEEE Transactions on Software Engineering - Special issue: best papers of the sixth international workshop on Petri nets and performance models (PNPM'95)
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Complete Decomposition of Stochastic Petri Nets Representing Generalized Service Networks
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IEEE Transactions on Software Engineering
A Notion of Equivalence for Stochastic Petri Nets
Proceedings of the 16th International Conference on Application and Theory of Petri Nets
A survey of equivalence notions for net based systems
Advances in Petri Nets 1992, The DEMON Project
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Papers from the 12th International Conference on Applications and Theory of Petri Nets: Advances in Petri Nets 1993
Superposed Generalized Stochastic Petri Nets: Definition and Efficient Solution
Proceedings of the 15th International Conference on Application and Theory of Petri Nets
Weak Bisimulation for Fully Probabilistic Processes
CAV '97 Proceedings of the 9th International Conference on Computer Aided Verification
Hierarchical Structuring of Superposed GSPNs
PNPM '97 Proceedings of the 6th International Workshop on Petri Nets and Performance Models
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Fundamenta Informaticae
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The use of Stochastic Petri Nets for performance analysis is limited by the state explosion of the underlying Continuous Time Markov Chain. A class of analysis methods to overcome this limitation are based on repeated decomposition and aggregation. In this paper, we propose a general framework for these kinds of solution methods and extend known techniques by introducing new classes of aggregates to reduce the approximation error. Aggregation relies on a formal definition of equivalence of Stochastic Petri Nets, which allows us to build aggregates at several levels of detail. The approach has been completely automated and allows the analysis of large and complex models with a low effort.