"On-the-fly" solution techniques for stochastic petri nets and extensions

Daniel D. Deavours, William H. Sanders

Research output: Contribution to journalArticlepeer-review

Abstract

High-level modeling representations, such as stochastic Petri nets, frequently generate very large state spaces and corresponding state-transition-rate matrices. In this paper, we propose a new steady-state solution approach that avoids explicit storing of the matrix in memory. This method does not impose any structural restrictions on the model, uses Gauss-Seidel and variants as the numerical solver, and uses less memory than current state-of-the-art solvers. An implementation of these ideas shows that one can realistically solve very large, general models in relatively little memory.

Original languageEnglish (US)
Pages (from-to)889-902
Number of pages14
JournalIEEE Transactions on Software Engineering
Volume24
Issue number10
DOIs
StatePublished - 1998

Keywords

  • Markov models
  • Matrix-free methods
  • Stochastic petri nets

ASJC Scopus subject areas

  • Software

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