Research Article
Deriving Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data
@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
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.
