Automated Customer-Centric Performance Analysis of Generalised Stochastic Petri Nets Using Tagged Tokens
File(s)entcs-paper.pdf (794.74 KB)
Accepted version
Author(s)
Knottenbelt, W
Dingle, N
Type
Journal Article
Abstract
Since tokens in Generalised Stochastic Petri Net (GSPN) models are indistinguishable, it is not always possible to reason about customer-centric performance measures. To remedy this, we propose ôtagged tokensö û a variant of the ôtagged customerö technique used in the analysis of queueing networks. Under this scheme, one token in a structurally restricted net is ôtaggedö and its position tracked as it moves around the net. Performance queries can then be phrased in terms of the position of the tagged token.\r\n\r\nTo date, the tagging of customers or tokens has been a time-consuming, manual and model-specific process. By contrast, we present here a completely automated methodology for the tagged token analysis of GSPNs. We first describe an intuitive graphical means of specifying the desired tagging configuration, along with the constraints on GSPN structure which must be observed for tagged tokens to be incorporated. We then present the mappings required for automatically converting a GSPN with a user-specified tagging structure into a Coloured GSPN (CGSPN), and thence into an unfolded GSPN which can be analysed for performance measures of interest by existing tools. We further show how our methodology integrates with Performance Trees, a formalism for the specification of performance queries.\r\n\r\nWe have implemented our approach in the open source PIPE Petri net tool, and use this to illustrate the extra expressibility granted by tagged tokens through the analysis of a GSPN model of a hospital's Accident and Emergency department.
Date Issued
2009-03
Citation
Electronic Notes in Theoretical Computer Science, 2009, 232, pp.75-88
ISSN
1571-0661
Publisher
Elsevier
Start Page
75
End Page
88
Journal / Book Title
Electronic Notes in Theoretical Computer Science
Volume
232
Copyright Statement
© 2009 Elsevier B.V. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Electronic Notes in Theoretical Computer Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, VOL:232,(2009) DOI:10.1016/j.entcs.2009.02.051
Source Volume Number
232