Abstract

Abstract 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 nonpreemptive 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 nonpreemptive 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 deadlines and utilizing a secondary criteria, such as minimizing the total execution time (or other metrics such as power or resource availability that can be easily extended) for scheduling tasks within a group, can lead to new and more efficient real-time scheduling algorithms.