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dc.contributor.authorCros, Fabrice
dc.contributor.authorKosmidis, Leonidas
dc.contributor.authorWartel, Franck
dc.contributor.authorMorales, David
dc.contributor.authorAbella, Jaume
dc.contributor.authorBroster, Ian
dc.contributor.authorCazorla, Francisco J.
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-05-17T14:20:37Z
dc.date.available2017-05-17T14:20:37Z
dc.date.issued2017-05-15
dc.identifier.citationCros, F. [et al.]. Dynamic software randomisation: Lessons learnec from an aerospace case study. A: "2017 Design, Automation & Test in Europe Conference & Exhibition (DATE)". 2017, p. 103-108.
dc.identifier.isbn978-3-9815370-8-6
dc.identifier.urihttp://hdl.handle.net/2117/104579
dc.description.abstractTiming Validation and Verification (V&V) is an important step in real-time system design, in which a system's timing behaviour is assessed via Worst Case Execution Time (WCET) estimation and scheduling analysis. For WCET estimation, measurement-based timing analysis (MBTA) techniques are widely-used and well-established in industrial environments. However, the advent of complex processors makes it more difficult for the user to provide evidence that the software is tested under stress conditions representative of those at system operation. Measurement-Based Probabilistic Timing Analysis (MBPTA) is a variant of MBTA followed by the PROXIMA European Project that facilitates formulating this representativeness argument. MBPTA requires certain properties to be applicable, which can be obtained by selectively injecting randomisation in platform's timing behaviour via hardware or software means. In this paper, we assess the effectiveness of the PROXIMA's dynamic software randomisation (DSR) with a space industrial case study executed on a real unmodified hardware platform and an industrial operating system. We present the challenges faced in its development, in order to achieve MBPTA compliance and the lessons learned from this process. Our results, obtained using a commercial timing analysis tool, indicate that DSR does not impact the average performance of the application, while it enables the use of MBPTA. This results in tighter pWCET estimates compared to current industrial practice.
dc.description.sponsorshipThe research leading to these results has received funding from the European Community’s FP7 [FP7/2007-2013] under the PROXIMA Project (www.proxima-project.eu), grant agreement no 611085. This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717.
dc.format.extent6 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshHardware
dc.subject.lcshSoftware
dc.subject.lcshTiming circuits--Design and construction--Data processing
dc.subject.otherSoftware
dc.subject.otherTiming
dc.subject.otherHardware
dc.subject.otherAerospace electronics
dc.subject.otherProbabilistic logic
dc.subject.otherRuntime
dc.subject.otherJitter
dc.titleDynamic software randomisation: Lessons learnec from an aerospace case study
dc.typeConference lecture
dc.subject.lemacOrdinadors--Dispositius de memòria
dc.subject.lemacProgramari
dc.identifier.doi10.23919/DATE.2017.7926966
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7926966/
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/RYC-2013-14717
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN/1PE/TIN2015-65316-P
upcommons.citation.publishedtrue
upcommons.citation.publicationName2017 Design, Automation & Test in Europe Conference & Exhibition (DATE)
upcommons.citation.startingPage103
upcommons.citation.endingPage108


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