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Power‐aware scheduling with effective task migration for real‐time multicore embedded systems
Author(s) -
March José Luis,
Sahuquillo Julio,
Petit Salvador,
Hassan Houcine,
Duato José
Publication year - 2012
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.2899
Subject(s) - frequency scaling , computer science , workload , multi core processor , energy consumption , scheduling (production processes) , dynamic voltage scaling , voltage , task (project management) , power management , real time computing , embedded system , power (physics) , distributed computing , energy (signal processing) , parallel computing , mathematical optimization , engineering , electrical engineering , operating system , statistics , physics , mathematics , systems engineering , quantum mechanics
SUMMARY A major design issue in embedded systems is reducing the power consumption because batteries have a limited energy budget. For this purpose, several techniques such as dynamic voltage and frequency scaling (DVFS) or task migration are being used. DVFS allows reducing power by selecting the optimal voltage supply, whereas task migration achieves this effect by balancing the workload among cores. This paper focuses on power‐aware scheduling allowing task migration to reduce energy consumption in multicore embedded systems implementing DVFS capabilities. To address energy savings, the devised schedulers follow two main rules: migrations are allowed at specific points of time and only one task is allowed to migrate each time. Two algorithms have been proposed working under real‐time constraints. The simpler algorithm, namely, single option migration (SOM) only checks just one target core before performing a migration. In contrast, the multiple option migration (MOM) searches the optimal target core. In general, the MOM algorithm achieves better energy savings than the SOM algorithm, although differences are wider for a reduced number of cores and frequency/voltage levels. Moreover, the MOM algorithm reduces energy consumption as much as 40% over the worst fit algorithm. Copyright © 2012 John Wiley & Sons, Ltd.