Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states
| العنوان: | Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states |
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| المؤلفون: | Stefan M. Petters, Geoffrey Nelissen, Muhammad Ali Awan, Patrick Meumeu Yomsi |
| المساهمون: | Repositório Científico do Instituto Politécnico do Porto |
| المصدر: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| بيانات النشر: | Springer Science and Business Media LLC, 2015. |
| سنة النشر: | 2015 |
| مصطلحات موضوعية: | Control and Optimization, Computer Networks and Communications, Computer science, Energy aware partitioning, 02 engineering and technology, Reduction (complexity), 0202 electrical engineering, electronic engineering, information engineering, System level energy management, Electrical and Electronic Engineering, Consumption (economics), Multi-core processor, DVFS and sleep states, business.industry, 020206 networking & telecommunications, Real-time embedded systems, Energy consumption, Heterogeneous platforms, 020202 computer hardware & architecture, Computer Science Applications, Power (physics), Core (game theory), Task-to-core mapping, Control and Systems Engineering, Modeling and Simulation, Embedded system, Heuristics, business, Energy (signal processing) |
| الوصف: | 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 $$18\,\%$$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. |
| وصف الملف: | application/pdf |
| تدمد: | 1573-1383 0922-6443 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8d4c727f3ad13a4bcdc365f1891f248Test https://doi.org/10.1007/s11241-015-9236-xTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....f8d4c727f3ad13a4bcdc365f1891f248 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15731383 09226443 |
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