Skip to main content
Log in

Implementing compositionality for stochastic Petri nets

  • Special section on the practical use of high-level Petri nets
  • Published:
International Journal on Software Tools for Technology Transfer Aims and scope Submit manuscript

Abstract.

An implementation of compositionality for stochastic well-formed nets (SWN) and, consequently, for generalized stochastic Petri nets (GSPN) has been recently included in the GreatSPN tool. Given two SWNs and a labelling function for places and transitions, it is possible to produce a third one as a superposition of places and transitions of equal label. Colour domains and arc functions of SWNs have to be treated appropriately. The main motivation for this extension was the need to evaluate a library of fault-tolerant “mechanisms” that have been recently defined, and are now under implementation, in a European project called TIRAN. The goal of the TIRAN project is to devise a portable software solution to the problem of fault tolerance in embedded systems, while the goal of the evaluation is to provide evidence of the efficacy of the proposed solution. Modularity being a natural “must” for the project, we have tried to reflect it in our modelling effort. In this paper, we discuss the implementation of compositionality in the GreatSPN tool, and we show its use for the modelling of one of the TIRAN mechanisms, the so-called local voter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Author information

Authors and Affiliations

Authors

Additional information

Published online: 24 August 2001

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bernardi, S., Donatelli, S. & Horváth, A. Implementing compositionality for stochastic Petri nets. STTT 3, 417–430 (2001). https://doi.org/10.1007/s100090100065

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s100090100065

Navigation