实时系统可调度性的周期和截止日期选择

Thidapat Chantem, Xiaofeng Wang, M. Lemmon, X. Hu
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引用次数: 28

摘要

任务周期适应通常用于缓解实时系统中的时间过载情况。现有框架假设只有任务周期是可调整的,任务截止日期始终保持不变。本文正式介绍了一种更通用的实时任务模型,其中任务期限是任务周期的函数,任务期限小于或等于任务周期。最近在控制系统中讨论了任务期限和任务周期之间的紧密耦合,并提出了一种新的实时调度挑战。为了解决周期和截止日期选择问题,本文确定了一个可行的周期和截止日期组合,并提出了一种启发式方法,该方法迭代地调整任务周期和截止日期,从而使任务集变得可调度。实验结果表明,启发式算法在73%的时间内找到了周期和截止日期选择问题的解,使用不到3次搜索迭代。当它无法找到问题的解决方案时,启发式在最坏情况下(最多100次搜索迭代)需要不到0.02s的运行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Period and Deadline Selection for Schedulability in Real-Time Systems
Task period adaptations are often used to alleviate temporal overload conditions in real-time systems. Existing frameworks assume that only task periods are adjustable and that task deadlines remain unchanged at all times. This paper formally introduces a more general real-time task model where task deadlines, which are less than or equal to task periods, are functions of task periods. This tight coupling between task deadlines and task periods has been discussed in a recent work in control systems and presents a novel real-time scheduling challenge. To solve the period and deadline selection problem, this article identifies a feasible period-deadline combination and proposes a heuristic, which iteratively adjusts task periods and deadlines in such a way that the task set becomes schedulable. Experimental results show that the heuristic finds a solution to the period and deadline selection problem over 73% of the time, using less than three search iterations. When it is unable to find a solution to the problem, the heuristic requires less than 0.02s to run in the worst-case (with at most 100 search iterations).
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