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

Muhammad Ali Awan, Patrick Meumeu Yomsi, Geoffrey Nelissen, Stefan M. Petters

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)

Abstract

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 (Formula presented.) 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.

Original languageEnglish
Pages (from-to)450-485
Number of pages36
JournalReal-Time Systems
Volume52
Issue number4
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

Keywords

  • DVFS and sleep states
  • Energy aware partitioning
  • Heterogeneous platforms
  • Real-time embedded systems
  • System level energy management
  • Task-to-core mapping

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