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 systemand 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.

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Stochastic performance models are widely used to analyse systems that involve the flow and processing of customers and resources. However, model formulation and parameterisation are traditionally manual and thus expensive, intrusive and error-prone. Our earlier work has demonstrated the feasibility of automated performance model construction from location tracking data. In particular, we presented a methodology based on a four-stage data processing pipeline, which automatically constructs Generalised Stochastic Petri Net (GSPN) performance models from an input dataset of raw location tracking traces. This pipeline was enhanced with a presence-based synchronisation detection mechanism.In this paper we introduce Coloured Generalised Stochastic Petri Nets (CGSPNs) into our methodology to provide support for multiple customer classes and service cycles. Distinct token types are used to model customers of different classes, while Johnson's algorithm for enumerating elementary cycles in a directed graph is employed to detect service cycles. Coloured tokens are also used to enforce accurate customer routing after the completion of a service cycle. We evaluate these extensions and their integration into the methodology via a case study of a simplified model of an Accident and Emergency (A&E) department. The case study is based on synthetic location tracking data, generated using an extended version of the LocTrackJINQS location-aware queueing network simulator.

The inference of performance models from low-level location tracking traces provides a means to gain high-level insight into customer and/or resource flow in complex systems. In this context our earlier work presented a methodology for automatically constructing Petri Net performance models from location tracking data. However, the capturing of synchronisation between service centres – the natural expression of which is one of the most fundamental advantages of Petri nets as a modelling formalism – was not explicitly supported. In this paper, we introduce mechanisms for automatically detecting and incorporating synchronisation into our existing methodology. We present a case study based on synthetic location tracking data where the derived synchronisation detection mechanism is applied

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.