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

被引:10
|
作者
Rodriguez, Ricardo J. [1 ]
Bernardi, Simona [2 ]
Zimmermann, Armin [3 ]
机构
[1] Gen Mil Acad, Ctr Univ Def, Zaragoza 50090, Spain
[2] Univ Zaragoza, Dept Informat & Ingn Sistemas, Zaragoza 50018, Spain
[3] Tech Univ Ilmenau, Dept Syst & Software Engn, D-98684 Ilmenau, Germany
基金
奥地利科学基金会; 欧盟地平线“2020”;
关键词
Benchmarking; generalized stochastic Petri nets (GSPNs); performance; simulation software; PERFORMANCE; DESIGN;
D O I
10.1109/TSMC.2018.2837643
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:2834 / 2844
页数:11
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