Cazorla, FJ.; Kosmidis, L.; Mezzetti, E.; Hernández Luz, C.; Abella, J.; Vardanega, T. (2019). Probabilistic Worst-Case Timing Analysis: Taxonomy and Comprehensive Survey. ACM Computing Surveys. 52(1):1-35. https://doi.org/10.1145/3301283
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/147545
Title:
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Probabilistic Worst-Case Timing Analysis: Taxonomy and Comprehensive Survey
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Author:
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Cazorla, Francisco J.
Kosmidis, L.
Mezzetti, E.
Hernández Luz, Carles
Abella, Jaume
Vardanega, Tullio
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UPV Unit:
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Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
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Issued date:
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Abstract:
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[EN] The unabated increase in the complexity of the hardware and software components of modern embedded real-time systems has given momentum to a host of research in the use of probabilistic and statistical techniques for ...[+]
[EN] The unabated increase in the complexity of the hardware and software components of modern embedded real-time systems has given momentum to a host of research in the use of probabilistic and statistical techniques for timing analysis. In the last few years, that front of investigation has yielded a body of scientific literature vast enough to warrant some comprehensive taxonomy of motivations, strategies of application, and directions of research. This survey addresses this very need, singling out the principal techniques in the state of the art of timing analysis that employ probabilistic reasoning at some level, building a taxonomy of them, discussing their relative merit and limitations, and the relations among them. In addition to offering a comprehensive foundation to savvy probabilistic timing analysis, this article also identifies the key challenges to be addressed to consolidate the scientific soundness and industrial viability of this emerging field.
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Subjects:
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Worst-case execution time
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Probabilistic analysis
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Copyrigths:
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Reserva de todos los derechos
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Source:
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ACM Computing Surveys. (issn:
0360-0300
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DOI:
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10.1145/3301283
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Publisher:
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Association for Computing Machinery
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Publisher version:
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https://doi.org/10.1145/3301283
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Project ID:
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info:eu-repo/grantAgreement/EC/H2020/772773/EU/Sustainable Performance for High-Performance Embedded Computing Systems/
info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/
info:eu-repo/grantAgreement/MINECO//RYC-2013-14717/ES/RYC-2013-14717/
info:eu-repo/grantAgreement/MINECO//IJCI-2016-27396/
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Description:
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"© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, {VOL 52, ISS 1, (February 2019)} https://dl.acm.org/doi/10.1145/3301283"
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Thanks:
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This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation ...[+]
This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773), and the HiPEAC Network of Excellence. Jaume Abella was partially supported by the Ministry of Economy and Competitiveness under a Ramon y Cajal postdoctoral fellowship (RYC-2013-14717). Enrico Mezzetti has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva-Incorporación postdoctoral fellowship No. IJCI-2016-27396.
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Type:
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Artículo
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