10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Testing spnps perfect sampling tool on fork-join queueing networks (tool paper)

  • @INPROCEEDINGS{10.4108/eai.25-10-2016.2266454,
        author={Simonetta Balsamo and Andrea Marin and Ivan Stojic},
        title={Testing spnps perfect sampling tool on fork-join queueing networks (tool paper)},
        proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2017},
        month={5},
        keywords={stochastic petri nets simulation perfect sampling},
        doi={10.4108/eai.25-10-2016.2266454}
    }
    
  • Simonetta Balsamo
    Andrea Marin
    Ivan Stojic
    Year: 2017
    Testing spnps perfect sampling tool on fork-join queueing networks (tool paper)
    VALUETOOLS
    ACM
    DOI: 10.4108/eai.25-10-2016.2266454
Simonetta Balsamo1, Andrea Marin1, Ivan Stojic1,*
  • 1: Universita Ca' Foscari Venezia
*Contact email: ivan.stojic@unive.it

Abstract

Stochastic Petri nets (SPNs) are widely used for the performance evaluation of computer and telecommunication systems. They inherit from their untimed version the capability of modeling parallel computations in a simple, graphical way. Simulation of SPNs is an important way to assess the performance of a system measured as throughput, response time or expected number of customers/resources in some places. Perfect sampling allows for the selection of the initial state of a simulation with a probability which corresponds to its stationary probability and hence the warm-up period is not required any more. In this paper we present performance tests of spnps, a tool based on some previous works that implements perfect sampling algorithm for SPNs by using decision diagrams. We test performance of the tool on a class of stochastic models of great importance in the quantitative evaluation of distributed systems and communication networks: the fork-join queueing networks.