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
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 environments 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 non-fair, earliest-deadline-first (EDF) global scheduling 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 EDF schemes provide a good mix of accuracy and average-case performance.
Work supported by NSF grants CCR 0204312, CNS 0309825, CNS 0408996, and CCF 0541056. The first author was also supported by an NSF fellowship.
Real-time systems that are adaptive in nature have received considerable recent attention [3, 11, 13, 5]. In addition, multiproces-
sor platforms are of growing importance, due to both hardware trends such as the emergence of multicore technologies and the
prevalence of computationally-intensive applications for which single-processor designs are not sufficient. In prior work [3, 5],
Block and colleagues considered the use of both fair and partitioning-based algorithms to schedule highly-adaptive workloads
on (tightly-coupled) multiprocessor platforms, where the processor shares of tasks change frequently and to a significant extent.