NDLI: Solution of Large GSPN Models

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Solution of Large GSPN Models

Content Provider	Scilit
Author	Ciardo, Gianfranco Trivedi, Kishor S.
Copyright Year	2021
Description	Generalized stochastic Petri nets (GSPNs) can be effectively used to represent many systems in a compact way. Efficient algorithms for the translation of a GSPN into its underlying continuous-time Markov chain (CTMC) are known and have been implemented in a number of software packages. While GSPNs relieve the modeler from the cumbersome and error-prone task of building and inputting the CTMC by hand, their analytical tractability is still limited by the combinatorial growth of the underlying CTMC. In order to avoid construction and solution of a large CTMC, we propose decomposing the GSPN into a set of subnets and separately solving individual subnets. Dependence among the subnets requires that, after solving each 566subnet, certain quantities be exported to other subnets. A fixed-point iteration is then used over the exported quantities. We discuss ways of decomposing a net into subnets, the type of quantities that need to be exchanged between subnets, and the convergence of the fixed-point iterative schemes. Book Name: Numerical Solution of Markov Chains
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003210160-30&type=chapterpdf
Ending Page	595
Page Count	31
Starting Page	565
DOI	10.1201/9781003210160-30
Language	English
Publisher	Informa UK Limited
Publisher Date	2021-06-16
Access Restriction	Open
Subject Keyword	Book Name: Numerical Solution of Markov Chains Automotive Engineering Packages Modeler Gspn Building Exported Solving Solution Ctmc Subnets
Content Type	Text
Resource Type	Chapter

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