Automated Construction of Petri Net Performance Models from High-Precision Location Tracking Data

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 highprecision 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. TheoutputperformancemodelcanbevisualisedusingPIPE2,theplatformindependent 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

Inferring Queueing Network Models from High-precision Location Tracking Data

Stochastic performance models are widely used to analyse the performance and reliability of systems that involve the flow and processing of customers. However, traditional methods of constructing a performance model are typically manual, time-consuming, intrusive and labour-intensive. The limited amount and low quality of manually-collected data often lead to an inaccurate picture of customer flows and poor estimates of model parameters. Driven by advances in wireless sensor technologies, recent real-time location systems (RTLSs) enable the automatic, continuous and unintrusive collection of high-precision location tracking data, in both indoor and outdoor environment. This high-quality data provides an ideal basis for the construction of high-fidelity performance models. This thesis presents a four-stage data processing pipeline which takes as input high-precision location tracking data and automatically constructs a queueing network performance model approximating the underlying system. The first two stages transform raw location traces into high-level “event logs” recording when and for how long a customer entity requests service from a server entity. The third stage infers the customer flow structure and extracts samples of time delays involved in the system; including service time, customer interarrival time and customer travelling time. The fourth stage parameterises the service process and customer arrival process of the final output queueing network model. To collect large-enough location traces for the purpose of inference by conducting physical experiments is expensive, labour-intensive and time-consuming. We thus developed LocTrack- JINQS, an open-source simulation library for constructing simulations with location awareness and generating synthetic location tracking data. Finally we examine the effectiveness of the data processing pipeline through four case studies based on both synthetic and real location tracking data. The results show that the methodology performs with moderate success in inferring multi-class queueing networks composed of single-server queues with FIFO, LIFO and priority-based service disciplines; it is also capable of inferring different routing policies, including simple probabilistic routing, class-based routing and shortest-queue routing