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An EDF-based Restricted-Migration Scheduling Algorithm for Multiprocessor Soft Real-Time Systems

Summary: An EDF-based Restricted-Migration Scheduling Algorithm
for Multiprocessor Soft Real-Time Systems
James H. Anderson, Vasile Bud, and UmaMaheswari C. Devi
Department of Computer Science
The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
There has been much recent interest in the use of the earliest-deadline-first (EDF) algorithm for scheduling
soft real-time sporadic task systems on identical multiprocessors. In hard real-time systems, a significant dis-
parity exists between EDF-based schemes and Pfair scheduling: on M processors, the worst-case schedulable
utilization for all known EDF variants is approximately M/2, whereas it is M for optimal Pfair algorithms. This
is unfortunate because EDF-based algorithms entail lower scheduling and task-migration overheads. However,
such a disparity in schedulability can be alleviated by easing the requirement that all deadlines be met, which
may be sufficient for soft real-time systems. In particular, in recent work, we have shown that if task migrations
are not restricted, then EDF (i.e., global EDF) can ensure bounded tardiness for a sporadic task system with
no restrictions on total utilization. Unrestricted task migrations in global EDF may be unappealing for some
systems, but if migrations are forbidden entirely, then bounded tardiness cannot be guaranteed. In this paper,
we address the issue of striking a balance between task migrations and system utilization by proposing an algo-
rithm called EDF-fm, which is based upon EDF and treads a middle path, by restricting, but not eliminating,
task migrations. Specifically, under EDF-fm, the ability to migrate is required for at most M - 1 tasks, and
it is sufficient that every such task migrate between two processors and at job boundaries only. EDF-fm, like


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


Collections: Computer Technologies and Information Sciences