Experiences using the UML profile for MARTE to stochastically model postproduction interactive applications
Experiences using the UML profile for MARTE to stochastically model postproduction interactive applications
Abstract: We describe a practical approach applying the UML 2.0 standard MARTE profile to model stochastic interactive application workflows, using the PapyrusUML editor. We use the PaStep, PaCommStep, PaLogicalResource and GaCommHost MARTE stereotypes and find them sufficient for stochastic modelling with the exception of being unable to define non-standard probability distributions. We have investigated both Markovian stochastic models and discrete event simulation models, serializing UML deployment and state machine diagrams to automate model creation. The choice between using a stochastic model (e.g. PRISM Markov models) or discrete event simulation model (e.g. Monte Carlo simulations) depends on the complexity of the model, accuracy required and compute time needed. We find that PRISM models are fast to execute if the complexity is small and produce numerically accurate results. Discrete event simulation models are slower to execute but scale much better and are probably the default solution to a model of unknown complexity.
Middleton, Stuart
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Servin, Arturo
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Zlatev, Zlatko
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Nasser, Bassem
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Papay, Juri
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Boniface, Mike
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Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Servin, Arturo
479a4d59-be8d-4d0a-90e4-82963b05e7f6
Zlatev, Zlatko
8f2e3635-d76c-46e2-85b9-53cc223fee01
Nasser, Bassem
d601c873-8295-44e3-a4e1-d363a26ee086
Papay, Juri
21652b35-de29-439c-b343-cb3437ef2f9e
Boniface, Mike
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Middleton, Stuart, Servin, Arturo, Zlatev, Zlatko, Nasser, Bassem, Papay, Juri and Boniface, Mike
(2010)
Experiences using the UML profile for MARTE to stochastically model postproduction interactive applications.
eChallenges 2010 Conference Proceedings, Warsaw, Poland.
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Conference or Workshop Item
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Abstract
Abstract: We describe a practical approach applying the UML 2.0 standard MARTE profile to model stochastic interactive application workflows, using the PapyrusUML editor. We use the PaStep, PaCommStep, PaLogicalResource and GaCommHost MARTE stereotypes and find them sufficient for stochastic modelling with the exception of being unable to define non-standard probability distributions. We have investigated both Markovian stochastic models and discrete event simulation models, serializing UML deployment and state machine diagrams to automate model creation. The choice between using a stochastic model (e.g. PRISM Markov models) or discrete event simulation model (e.g. Monte Carlo simulations) depends on the complexity of the model, accuracy required and compute time needed. We find that PRISM models are fast to execute if the complexity is small and produce numerically accurate results. Discrete event simulation models are slower to execute but scale much better and are probably the default solution to a model of unknown complexity.
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Submitted date: 2010
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Event Dates: October 2010
Venue - Dates:
eChallenges 2010 Conference Proceedings, Warsaw, Poland, 2010-10-01
Organisations:
Electronics & Computer Science, IT Innovation
Identifiers
Local EPrints ID: 271076
URI: http://eprints.soton.ac.uk/id/eprint/271076
PURE UUID: 926d93bb-046f-4cb3-be74-9876bc03b5ec
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Date deposited: 13 May 2010 14:58
Last modified: 15 Mar 2024 03:09
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Contributors
Author:
Arturo Servin
Author:
Zlatko Zlatev
Author:
Bassem Nasser
Author:
Juri Papay
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