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Response Time Analysis of Sporadic DAG Tasks under Partitioned Scheduling

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dc.contributor.authorFonseca, José
dc.contributor.authorNelissen, Geoffrey
dc.contributor.authorNelis, Vincent
dc.contributor.authorPinho, Luís Miguel
dc.date.accessioned2017-01-26T12:07:44Z
dc.date.embargo2117
dc.date.issued2016
dc.description11th IEEE International Symposium on Industrial Embedded Systems (SIES 2016). 23 to 25, May, 2016. Krakow, Poland.pt_PT
dc.description.abstractSeveral schedulability analyses have been proposed for a variety of parallel task systems with real-time constraints. However, these analyses are mostly restricted to global scheduling policies. The problem with global scheduling is that it adds uncertainty to the lower-level timing analysis which on multicore systems are heavily context-dependent. As parallel tasks typically exhibit intense communication and concurrency among their sequential computational units, this problem is further exacerbated. This paper considers instead the schedulability of partitioned parallel tasks. More precisely, we present a response time analysis for sporadic DAG tasks atop multiprocessors under partitioned fixed-priority scheduling. We assume the partitioning to be given. We show that a partitioned DAG task can be modeled as a set of self-suspending tasks. We then propose an algorithm to traverse a DAG and characterize such worst-case scheduling scenario. With minor modifications, any state-of-the-art technique for sporadic self-suspending tasks can thus be used to derived the worstcase response time of a partitioned DAG task. Experiments show that the proposed approach significantly tightens the worst-case response time of partitioned parallel tasks comparatively to the state-of-the-art when the most accurate technique is chosen.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/SIES.2016.7509443pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9432
dc.language.isoengpt_PT
dc.publisherInstitute of Electrical and Electronics Engineerspt_PT
dc.relationParallel SOftware framework for time-CRitical mAny-core sysTEmS
dc.relationGuaranteed Component Assembly with Round Trip Analysis for Energy Efficient High-integrity Multi-core Systems
dc.relation.ispartofseriesSIES;2016
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7509443/pt_PT
dc.subjectProcessor schedulingpt_PT
dc.subjectTime factorspt_PT
dc.subjectReal-time systemspt_PT
dc.subjectTimingpt_PT
dc.subjectMulticore processingpt_PT
dc.subjectComputational modelingpt_PT
dc.subjectInterferencept_PT
dc.titleResponse Time Analysis of Sporadic DAG Tasks under Partitioned Schedulingpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleParallel SOftware framework for time-CRitical mAny-core sysTEmS
oaire.awardTitleGuaranteed Component Assembly with Round Trip Analysis for Energy Efficient High-integrity Multi-core Systems
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/611016/EU
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/333053/EU
oaire.citation.conferencePlaceKrakow, Polandpt_PT
oaire.citation.endPage12pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title11th IEEE Symposium on Industrial Embedded Systems, 2016pt_PT
oaire.fundingStreamFP7
oaire.fundingStreamFP7
person.familyNamePinho
person.givenNameLuis Miguel
person.identifier.ciencia-id8112-2108-F3B2
person.identifier.orcid0000-0001-6888-1340
person.identifier.ridM-3416-2013
person.identifier.scopus-author-id6602594556
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
project.funder.nameEuropean Commission
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationfd791145-af93-47d9-bbe8-647a326d2f39
relation.isAuthorOfPublication.latestForDiscoveryfd791145-af93-47d9-bbe8-647a326d2f39
relation.isProjectOfPublication88e3c263-adf4-4fb5-bb99-418ffe5993ed
relation.isProjectOfPublication17322fe8-c558-4180-88af-8ed0b847498c
relation.isProjectOfPublication.latestForDiscovery17322fe8-c558-4180-88af-8ed0b847498c

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