An efficient two-stage iterative method for the steady-state analysis of Markov regenerative stochastic Petri net models

Luai M. Malhis, William H. Sanders

Research output: Contribution to journalArticlepeer-review

Abstract

To enhance the modeling power of stochastic Petri nets (SPNs), new steady-state analysis methods have been proposed for nets that include non-exponential transitions. The underlying stochastic process is a Markov regenerative process (MRP) when at most one non-exponential transition is enabled in each marking. Time-efficient algorithms for constructing and solving the MRP have been developed. However, the space required to solve such models is often extremely large. This largeness is due to the large number of transitions in the MRP. Traditional analysis methods require that all these transitions be stored in primary memory for efficient computation. If the size of available memory is smaller than that needed to store these transitions, a time-efficient computation is impossible using these methods. To use this class of SPNs to model realistic systems, the space complexity of MRP analysis algorithms must be reduced. In this paper, we propose a new steady-state analysis method that is both time and space efficient. The new method takes advantage of the structure of the underlying process to reduce both computation time and required memory. The performance of the proposed method is compared to existing methods using several SPN examples.

Original languageEnglish (US)
Pages (from-to)583-601
Number of pages19
JournalPerformance Evaluation
Volume27-28
StatePublished - Oct 1996

Keywords

  • Deterministic stochastic Petri nets
  • Iterative solution methods
  • Markov chains
  • Markov regenerative stochastic Petri nets
  • Stochastic activity networks

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'An efficient two-stage iterative method for the steady-state analysis of Markov regenerative stochastic Petri net models'. Together they form a unique fingerprint.

Cite this