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dc.contributor.authorCazorla, Francisco J.
dc.contributor.authorKosmidis, Leonidas
dc.contributor.authorMezzetti, Enrico
dc.contributor.authorHernandez, Carles
dc.contributor.authorAbella Ferrer, Jaume
dc.contributor.authorVardanega, Tullio
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2019-07-25T08:18:57Z
dc.date.available2019-07-25T08:18:57Z
dc.date.issued2019-02-01
dc.identifier.citationCazorla, F. J. [et al.]. Probabilistic Worst-Case Timing Analysis: Taxonomy and Comprehensive Survey. "ACM Computing Surveys (CSUR)", 1 Febrer 2019, vol. 52, núm. 1.
dc.identifier.issn0360-0300
dc.identifier.urihttp://hdl.handle.net/2117/166847
dc.description.abstractThe 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.
dc.description.sponsorshipThis 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 pro-gramme (grant agreement No. 772773), and the HiPEAC Network of Excellence. Jaume Abella was partially supportedby the Ministry of Economy and Competitiveness under a Ramon y Cajal postdoctoral fellowship (RYC-2013-14717). En-rico Mezzetti has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de laCierva-Incorporación postdoctoral fellowship No. IJCI-2016-27396.
dc.format.extent35
dc.language.isoeng
dc.publisherACM
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshHigh performance computing
dc.subject.otherProbabilistic representations
dc.subject.otherEmbedded systems
dc.subject.otherSoftware verification and validation
dc.subject.otherReal-time systems
dc.titleProbabilistic Worst-Case Timing Analysis: Taxonomy and Comprehensive Survey
dc.typeArticle
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1145/3301283
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?doid=3309872.3301283
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/772773/EU/Sustainable Performance for High-Performance Embedded Computing Systems/SuPerCom
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//RYC-2013-14717/ES/RYC-2013-14717/
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/PE2013-2016/IJCI-2016-27396
local.citation.publicationNameACM Computing Surveys (CSUR)
local.citation.volume52
local.citation.number1


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