Deriving generalised stochastic Petri net performance models from high-precision location tracking data

  • Authors:

  • Nikolas Anastasiou;Tzu-Ching Horng;William Knottenbelt

  • Affiliations:

  • Imperial College London, South Kensington Campus, London;Imperial College London, South Kensington Campus, London;Imperial College London, South Kensington Campus, London

  • Venue:
  • Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
  • Year:
  • 2011

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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.