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A non-preemptive scheduling algorithm for soft real-time systems

Summary: A non-preemptive scheduling algorithm for soft
real-time systems
Wenming Li, Krishna Kavi *, Robert Akl
The University of North Texas, Computer Science and Engineering, P.O. Box, 311366, Denton TX 76203, United States
Received 2 December 2005; accepted 10 April 2006
Real-time systems are often designed using preemptive scheduling and worst-case execution time estimates to guarantee
the execution of high priority tasks. There is, however, an interest in exploring non-preemptive scheduling models for real-
time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses
multiple scheduling strategies for efficient non-preemptive scheduling of tasks. Our goal is to improve the success ratio of
the well-known Earliest Deadline First (EDF) approach when the load on the system is very high and to improve the overall
performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF) is based on
dynamic grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique
to schedule tasks within the group. We will present results comparing gEDF with other real-time algorithms including,
EDF, Best-effort, and Guarantee, by using randomly generated tasks with varying execution times, release times, deadlines
and tolerance to missing deadlines, under varying workloads. We believe that grouping tasks dynamically with similar dead-
lines and utilizing a secondary criteria, such as minimizing the total execution time (or other metrics such as power or
resource availability) for scheduling tasks within a group, can lead to new and more efficient real-time scheduling algorithms.
2006 Elsevier Ltd. All rights reserved.
Keywords: Soft real-time systems; Non-preemptive real-time scheduling; Earliest Deadline First (EDF); Shortest Job First (SJF); Best-


Source: Akl, Robert - Department of Computer Science and Engineering, University of North Texas


Collections: Computer Technologies and Information Sciences