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Fine-Grained Task Reweighting on Multiprocessors Aaron Block, James H. Anderson, and Gary Bishop
 

Summary: Fine-Grained Task Reweighting on Multiprocessors
Aaron Block, James H. Anderson, and Gary Bishop
Department of Computer Science­University of North Carolina at Chapel Hill
Abstract
We consider the problem of task reweighting in fair-
scheduled multiprocessor systems wherein each task's pro-
cessor share is specified as a weight. Task reweighting can
be used as a means for consuming (or making available)
spare processing capacity. In this paper, we propose a mul-
tiprocessor reweighting scheme that can change a task's
processor share with "minimal" error per share change.
1 Introduction
Two trends are evident in recent work on real-time sys-
tems. First, multiprocessor designs are becoming quite
common. This is due both to the advent of reasonably-
priced multiprocessor platforms and to the prevalence of
computationally-intensive applications with real-time re-
quirements that have pushed beyond the capabilities of
single-processor systems. Second, many applications now
exist that require fine-grained adaptivity, i.e., the ability

  

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

 

Collections: Computer Technologies and Information Sciences