Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/6692
Título: Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states
Autor: Awan, Muhammad Ali
Yomsi, Patrick Meumeu
Nelissen, Geoffrey
Petters, Stefan M.
Palavras-chave: Energy aware partitioning
DVFS and sleep states
Task-to-core mapping
Heterogeneous platforms
Real-time embedded systems
System level energy management
Data: 2016
Editora: Springer
Relatório da Série N.º: Real-Time Systems;Vol.52, nº4
Resumo: Heterogeneous 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.
Peer review: yes
URI: http://hdl.handle.net/10400.22/6692
DOI: 10.1007/s11241-015-9236-x
ISSN: 0922-6443
1573-1383
Versão do Editor: http://link.springer.com/article/10.1007%2Fs11241-015-9236-x#/page-1
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