Summary: Fine­Grained Task Reweighting on Multiprocessors #
Aaron Block, James H. Anderson, and Gary Bishop
Department of Computer Science, University of North Carolina at Chapel Hill
{block, anderson, gb}@cs.unc.edu
Abstract
We consider the problem of task reweighting in fair­scheduled multiprocessor systems wherein each task's processor share
is specified as a weight. When a task is reweighted, a new weight is computed for it, which is then used in future scheduling.
Task reweighting can be used as a means for consuming (or making available) spare processing capacity. The responsiveness of
a reweighting scheme can be assessed by comparing its allocations to those of an ideal scheduler that can reweight tasks instan­
taneously. A reweighting scheme is fine­grained if any additional per­task ``error'' (in comparison to an ideal allocation) caused
by a reweighting event is constant. In prior work on uniprocessor notions of fairness, a number of fine­grained reweighting
schemes were proposed. However, in the multiprocessor case, prior work has failed to produce such a scheme. In this paper, we
remedy this shortcoming by presenting a multiprocessor reweighting scheme that is fine­grained. We also present an experimen­
tal evaluation of this scheme that shows that it is often much more responsive than prior (non­fine­grained) schemes in enacting
weight­change requests.
Keywords: Adaptive, multiprocessor, Pfair, reweighting
# Work supported by NSF grants CCR 0204312, CNS 0309825, CNS 0408996, and CCF 0541056. The first author was also supported by an NSF fellowship.
A preliminary version of this paper appeared in Proceedings of the 11th IEEE International Conference on Embedded and Real­Time Computing Systems and
Applications, pages 429­435, August 2005


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

Collections: Computer Technologies and Information Sciences