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Title: Automated construction of Petri net performance models from high-precision location tracking data
Author: Anastasiou, Nikolas
ISNI:       0000 0004 2741 6591
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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Stochastic performance models are widely used to analyse the performance and reliability of 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. This thesis illustrates the feasibility of automated performance model construction from high-precision location tracking data. In particular, we present a methodology based on a four-stage data processing pipeline which automatically constructs Coloured Generalised Stochastic Petri Net (CGSPN) performance models from an input dataset consisting of raw location tracking traces. The output performance model can be visualised using PIPE2, the platform independent Petri Net editor. The developed methodology can be applied to customer-processing systems which support multiple customers classes and can capture the initial and inter-routing probability of the customer flow of the underlying system. Furthermore, it detects any presence-based synchronisation conditions that may be inherent in the underlying system and the presence of service cycles. Service time distributions, one for each customer class, of each service area in the system and travelling time distributions between pairs of service areas are also characterised. PEPERCORN, the tool that implements the developed methodology, is also presented. In addition to the latter, this thesis presents LocTrackJINQS, the extensible, location-aware Queueing Network simulator. LocTrackJINQS was developed to support location-based research. It has the ability to simulate a user-specified Queueing Network and while simulation progresses, it generates and outputs location tracking data – associated with the movement of the customers in the network – in a trace file. Our methodology is evaluated through six case studies. These case studies use synthetic location tracking data generated by LocTrackJINQS. The obtained results suggest that the methodology can infer the abstract structure of the system – specified in terms of the locations and service radii of the system’s service areas (max error 0.320 m and 0.277 m respectively) and customer flow – and approximate its service time delays well. In fact, the maximum relative entropy value that was obtained between the simulated and inferred service time distributions is 0.324 nats. Furthermore, whenever synchronisation between service areas takes place, the simulated synchronisation conditions are successfully inferred.
Supervisor: Knottenbelt, William ; Harrison, Peter Sponsor: Engineering and Physical Sciences Research Council ; Imperial College London
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral