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 language | English (US) |
---|---|
Pages (from-to) | 889-902 |
Number of pages | 14 |
Journal | IEEE Transactions on Software Engineering |
Volume | 24 |
Issue number | 10 |
DOIs | |
State | Published - 1998 |
Externally published | Yes |
Keywords
- Markov models
- Matrix-free methods
- Stochastic petri nets
ASJC Scopus subject areas
- Software