4.7 Article

An Evaluation Framework for Comparative Analysis of Generalized Stochastic Petri Net Simulation Techniques

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2837643

关键词

Benchmarking; generalized stochastic Petri nets (GSPNs); performance; simulation software

资金

  1. EU Horizon 2020 Research and Innovation Programme [644869]
  2. Spanish Ministry of Economy, Industry, and Competitiveness [TIN2014-58457-R]
  3. Aragon Government-DisCo Research Group [T27]
  4. Austrian Science Fund (FWF) [T27] Funding Source: Austrian Science Fund (FWF)

向作者/读者索取更多资源

Availability of a common, shared benchmark to provide repeatable, quantifiable, and comparable results is an added value for any scientific community. International consortia provide benchmarks in a wide range of domains, being normally used by industry, vendors, and researchers for evaluating their software products. In this regard, a benchmark of untimed Petri net models was developed to be used in a yearly software competition driven by the Petri net community. However, to the best of our knowledge there is not a similar benchmark to evaluate solution techniques for Petri nets with timing extensions. In this paper, we propose an evaluation framework for the comparative analysis of generalized stochastic Petri nets (GSPNs) simulation techniques. Although we focus on simulation techniques, our framework provides a baseline for a comparative analysis of different GSPN solvers (e.g., simulators, numerical solvers, or other techniques). The evaluation framework encompasses a set of 50 GSPN models including test cases and case studies from the literature, and a set of evaluation guidelines for the comparative analysis. In order to show the applicability of the proposed framework, we carry out a comparative analysis of steady-state simulators implemented in three academic software tools, namely, GreatSPN, PeabraiN, and TimeNET. The results allow us to validate the trustfulness of these academic software tools, as well as to point out potential problems and algorithmic optimization opportunities.

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