ACM Transactions on Computer Systems (TOCS)
Automated construction of GSPN models for flexible manufacturing systems
Computers in Industry - special issue ASI'94 selection of papers presented at the advanced summer institute “computer integrated manufacturing and industrial automation” Patras, Greece, 26 June—1 July 1994
A Novel Approach for Phase-Type Fitting with the EM Algorithm
IEEE Transactions on Dependable and Secure Computing
PIPE2: a tool for the performance evaluation of generalised stochastic Petri Nets
ACM SIGMETRICS Performance Evaluation Review
Automated simulation-based capacity planning for enterprise data fabrics
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
PEPERCORN: inferring performance models from location tracking data
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
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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.