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Task Reweighting under Global Scheduling on Multiprocessors Aaron Block, James H. Anderson, and UmaMaheswari C. Devi
 

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 platforms. Our
particular focus is reweighting schemes that are deployed in envi-
ronments in which tasks may frequently request significant share
changes. Prior work has shown that fair scheduling algorithms are
capable of reweighting tasks with minimal allocation error and
that partitioning-based scheduling algorithms can reweight tasks
with better average-case performance, but greater error. However,
preemption and migration overheads can be high in fair schemes.
In this paper, we consider the question of whether global schedul-
ing techniques 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 pro-
vide a good mix of accuracy and average-case performance.
1 Introduction

  

Source: Anderson, James - Department of Computer Science, University of North Carolina at Chapel Hill

 

Collections: Computer Technologies and Information Sciences