5th International ICST Conference on Performance Evaluation Methodologies and Tools

Research Article

Deriving Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data

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  • @INPROCEEDINGS{10.4108/icst.valuetools.2011.245715,
        author={Nikolas Anastasiou and Tzu-Ching Horng and William Knottenbelt},
        title={Deriving Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data},
        proceedings={5th International ICST Conference on Performance Evaluation Methodologies and Tools},
        publisher={ICST},
        proceedings_a={VALUETOOLS},
        year={2012},
        month={6},
        keywords={Location Tracking Performance Modelling Data Mining Generalised Stochastic Petri Nets},
        doi={10.4108/icst.valuetools.2011.245715}
    }
    
  • Nikolas Anastasiou
    Tzu-Ching Horng
    William Knottenbelt
    Year: 2012
    Deriving Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data
    VALUETOOLS
    ICST
    DOI: 10.4108/icst.valuetools.2011.245715
Nikolas Anastasiou1,*, Tzu-Ching Horng1, William Knottenbelt1
  • 1: Imperial College London
*Contact email: na405@doc.ic.ac.uk

Abstract

Stochastic performance models have been widely used to analyse the performance and reliability of systems that involve the flow and processing of customers and/or resources with multiple service centres. However, the quality of performance analysis delivered by a model depends critically on the degree to which the model accurately represents the operations of the real system. This paper presents an automated technique which takes as input high-precision location tracking data – potentially collected from a real life system – and constructs a hierarchical Generalised Stochastic Petri Net performance model of the underlying system. We examine our method’s effectiveness and accuracy through two case studies based on synthetic location tracking data.