Abstract
Stochastic performance models are widely used to analyse the performance of systems that process customers and resources. However, the construction of such models is traditionally manual and therefore expensive, intrusive and prone to human error. In this paper we introduce PEPERCORN, a Petri Net Performance Model (PNPM) construction tool, which, given a dataset of raw location tracking traces obtained from a customer-processing system, automatically formulates and parameterises a corresponding Coloured Generalised Stochastic Petri Net (CGSPN) performance model.
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Anastasiou, N., Knottenbelt, W. (2013). PEPERCORN: Inferring Performance Models from Location Tracking Data. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds) Quantitative Evaluation of Systems. QEST 2013. Lecture Notes in Computer Science, vol 8054. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40196-1_14
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DOI: https://doi.org/10.1007/978-3-642-40196-1_14
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