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Title
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Reducing computational cost of large-scale simulations using opportunistic model approximation
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Author
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Abstract
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| We present a dynamic model approximation strategy that allows to significantly increase computational efficiency of the simulation while maintaining proper validity. This can be used to effectively overcome the scalability constraints in state-of-the-art simulation frameworks for testing and validating large-scale systems. The method that we present leverages information theory metrics to measure the possible contribution of sub-areas in the simulation to the global behavior. This allows us to opportunistically approximate low-contributing areas and as a result decrease the computational cost of the simulation. We present a basic traffic-simulation use-case, implemented in the Acsim simulator to validate the proposed method and are able to achieve a 33% reduction of the computational cost. Furthermore, we analyze our proposed method from a more theoretical perspective. |
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Language
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English
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Source (book)
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2019 Spring Simulation Conference (SpringSim), 29 April-2 May, 2019, Tucson, Arizona, USA
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Publication
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IEEE
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2019
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ISBN
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978-1-7281-3547-2
978-1-5108-8388-8
[electronic]
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DOI
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10.23919/SPRINGSIM.2019.8732848
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Volume/pages
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(2019)
, 12 p.
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ISI
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000492000800003
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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