11th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

An introduction to the ORIS tool

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.4108/eai.5-12-2017.2275046,
    author={Marco  Biagi and Laura  Carnevali and Marco  Paolieri and Enrico  Vicario},
    title={An introduction to the ORIS tool},
    proceedings={11th EAI International Conference on Performance Evaluation Methodologies and Tools},
    publisher={ACM},
    proceedings_a={VALUETOOLS},
    year={2018},
    month={8},
    keywords={tools stochastic petri nets markov regenerative processes non-markovian processes transient analysis steady-state probabilities},
    doi={10.4108/eai.5-12-2017.2275046}
}
  • Marco Biagi
    Laura Carnevali
    Marco Paolieri
    Enrico Vicario
    Year: 2018
    An introduction to the ORIS tool
    VALUETOOLS
    ACM
    DOI: 10.4108/eai.5-12-2017.2275046
Marco Biagi1, Laura Carnevali1,*, Marco Paolieri2, Enrico Vicario1
  • 1: Department of Information Engineering - University of Florence
  • 2: Department of Computer Science - University of Southern California
*Contact email: laura.carnevali@unifi.it

Abstract

ORIS provides a graphical interface to draw Petri nets, analysis engines for different classes of underlying stochastic process, and visualization of reward-based metrics. It also includes a Java API for model definition and analysis, which can be used to carry out parametric performance studies. ORIS implements methods for steady-state and transient analysis of Semi-Markov Processes (SMPs), Markov Regenerative Processes (MRPs), Generalized Semi-Markov Processes~(GSMPs), and Continuous-Time Markov Chains (CTMCs).

Keywords
tools, stochastic petri nets, markov regenerative processes, non-markovian processes, transient analysis, steady-state probabilities
Published
2018-08-10
Publisher
ACM
http://dx.doi.org/10.4108/eai.5-12-2017.2275046
Copyright © 2017–2025 ACM
EAI Logo

About EAI

Community

Publish with EAI