PEPERCORN: Inferring Performance Models from Location Tracking Data

Provided by: Imperial College London
Topic: Big Data
Format: PDF
Stochastic performance models are widely used to analyze 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, the authors 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 parameterizes a corresponding Colored Generalized Stochastic Petri Net (CGSPN) performance model.

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