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Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

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dc.contributor.authorAwan, Muhammad Ali
dc.contributor.authorYomsi, Patrick Meumeu
dc.contributor.authorNelissen, Geoffrey
dc.contributor.authorPetters, Stefan M.
dc.date.accessioned2015-10-15T10:24:27Z
dc.date.available2015-10-15T10:24:27Z
dc.date.issued2016
dc.description.abstractHeterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.pt_PT
dc.identifier.doi10.1007/s11241-015-9236-x
dc.identifier.issn0922-6443
dc.identifier.issn1573-1383
dc.identifier.urihttp://hdl.handle.net/10400.22/6692
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationUID/CEC/04234/2013 (CISTER Research Centre)pt_PT
dc.relationARTEMIS/0003/2012, JU Grant No. 333053 (CONCERTO)pt_PT
dc.relationARTEMIS/0001/2013 - JU Grant No. 621429 (EMC2)pt_PT
dc.relationFP7/2007-2013, Grant Agreement No. 611016 (P-SOCRATES)pt_PT
dc.relation.ispartofseriesReal-Time Systems;Vol.52, nº4
dc.relation.publisherversionhttp://link.springer.com/article/10.1007%2Fs11241-015-9236-x#/page-1pt_PT
dc.subjectEnergy aware partitioningpt_PT
dc.subjectDVFS and sleep statespt_PT
dc.subjectTask-to-core mappingpt_PT
dc.subjectHeterogeneous platformspt_PT
dc.subjectReal-time embedded systemspt_PT
dc.subjectSystem level energy managementpt_PT
dc.titleEnergy-aware task mapping onto heterogeneous platforms using DVFS and sleep statespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage485pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage450pt_PT
oaire.citation.titleReal-Time Systemspt_PT
oaire.citation.volume52pt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT

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