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Trade-offs between voltage scaling and processor shutdown for low-energy embedded multiprocessors

Published: 16 July 2007 Publication History

Abstract

When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption is expected to increase significantly. Then it will be more effective to limit the number of employed processors, and use a combination of DVS and processor shutdown. Scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results show that our approach reduces the total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS.We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.

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Cited By

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  • (2012)Comparison of scheduling schemes for on-demand IaaS requestsJournal of Systems and Software10.1016/j.jss.2012.01.01985:6(1400-1408)Online publication date: 1-Jun-2012
  • (2010)Linear Combinations of DVFS-Enabled Processor Frequencies to Modify the Energy-Aware Scheduling AlgorithmsProceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing10.1109/CCGRID.2010.38(388-397)Online publication date: 17-May-2010

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cover image Guide Proceedings
SAMOS'07: Proceedings of the 7th international conference on Embedded computer systems: architectures, modeling, and simulation
July 2007
465 pages
ISBN:3540736220

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 16 July 2007

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View all
  • (2012)Comparison of scheduling schemes for on-demand IaaS requestsJournal of Systems and Software10.1016/j.jss.2012.01.01985:6(1400-1408)Online publication date: 1-Jun-2012
  • (2010)Linear Combinations of DVFS-Enabled Processor Frequencies to Modify the Energy-Aware Scheduling AlgorithmsProceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing10.1109/CCGRID.2010.38(388-397)Online publication date: 17-May-2010

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