inis 16(8): e4

Research Article

Parallel Simulation of Queueing Petri Nets

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  • @ARTICLE{10.4108/eai.24-8-2015.2261102,
        author={J\'{y}rgen Walter and Simon Spinner and Samuel Kounev},
        title={Parallel Simulation of Queueing Petri Nets},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={3},
        number={8},
        publisher={ACM},
        journal_a={INIS},
        year={2015},
        month={8},
        keywords={parallel discrete event simulation, performance prediction, stochastic performance modeling, queueing petri nets},
        doi={10.4108/eai.24-8-2015.2261102}
    }
    
  • Jürgen Walter
    Simon Spinner
    Samuel Kounev
    Year: 2015
    Parallel Simulation of Queueing Petri Nets
    INIS
    EAI
    DOI: 10.4108/eai.24-8-2015.2261102
Jürgen Walter1,*, Simon Spinner1, Samuel Kounev1
  • 1: University of Würzburg
*Contact email: juergen.walter@uni-wuerzburg.de

Abstract

Queueing Petri Nets (QPNs) are a powerful formalism to model the performance of software systems. Such models can be solved using analytical or simulation techniques. Analytical techniques suffer from scalability issues, whereas simulation techniques often require very long simulation runs. Existing simulation techniques for QPNs are strictly sequential and cannot exploit the parallelism provided by modern multi-core processors. In this paper, we present an approach to parallel discrete-event simulation of QPNs using a conservative synchronization algorithm. We consider the spatial decomposition of QPNs as well as the lookahead calculation for different scheduling strategies. Additionally, we propose techniques to reduce the synchronization overhead when simulating performance models describing systems with open workloads. The approach is evaluated in three case studies using performance models of real-world software systems. We observe speedups between 1.9 and 2.5 for these case studies. We also assessed the maximum speedup that can be achieved with our approach using synthetic models.