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Summary: Task Reweighting under Global Scheduling on Multiprocessors
Aaron Block, James H. Anderson, and UmaMaheswari C. Devi
Department of Computer Science, University of North Carolina at Chapel Hill
Abstract
We consider schemes for enacting task share changes--a
process called reweighting--on real-time multiprocessor plat-
forms. Our particular focus is reweighting schemes that are
deployed in environments in which tasks may frequently re-
quest significant share changes. Prior work has shown that fair
scheduling algorithms are capable of reweighting tasks with
minimal allocation error and that partitioning-based schedul-
ing algorithms can reweight tasks with better average-case per-
formance, but greater error. However, preemption and migra-
tion overheads can be high in fair schemes. In this paper,
we consider the question of whether global scheduling tech-
niques can improve the accuracy of reweighting relative to
partitioning-based schemes and provide improved average-case
performance relative to fair-scheduled systems. Our conclusion
is that, for soft real-time systems, global scheduling techniques
provide a good mix of accuracy and average-case performance.
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