周期性实时任务的非抢占式多处理机调度

Jaishree Mayank, Arijit Mondal
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引用次数: 3

摘要

在多处理器环境中调度一组任务是一项计算密集型的工作。在多处理器上调度任务主要有两种广泛的方法。在其中一种方法中,任务在开始时分配给处理器(基于分区的策略),而其他方法维护全局调度器。在多处理器调度中存在着大量的工作,它们具有不同的优化目标,如调度长度、响应时间、处理器利用率等。然而,这些工作大多集中在抢占调度上。对非抢占式任务的调度很少关注。在这项工作中,我们提出了一种使用最少处理器数量调度一组非抢占式实时任务的方法。我们考虑使用最适合或最适合的装箱策略将任务分配给处理器,并对每个处理器应用非抢占的最早截止日期优先(npEDF)调度方法。我们通过将不同的分区策略与任务的顺序(周期、利用率等)结合起来进行了大量的实验。结果表明:第一拟合递增期、最拟合递增期、第一拟合递减利用率和最拟合递减利用率均具有较好的效果。减少利用率启发式的成功率比增加周期启发式的成功率高10% ~ 30%。我们观察到首次拟合减少利用率和最佳拟合减少利用率比首次拟合增加期和最佳拟合增加期需要更多的时间。我们还将我们的结果与现有的方法进行了比较。
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Non-preemptive multiprocessor scheduling for periodic real-time tasks
Scheduling of a set of tasks in multiprocessor environment is a computationally intensive job. There are primarily two broad approaches for scheduling of tasks on multiprocessors. In one of the approaches tasks are allocated to processor in the beginning (partition based strategy) and the other approaches maintain a global scheduler. There exist a large volume of work in multiprocessor scheduling having different optimization objectives such as schedule length, response time, processor utilization, etc. However, most of these works focus only in preemptive scheduling. Very less attention has been given for scheduling of non-preemptive tasks. In this work, we present a methodology for scheduling of a set of non-preemptive real-time tasks using minimum number of processors. We consider that the tasks are allocated to the processors using bin-packing strategies such as firstfit or best-fit and non-preemptive Earliest Deadline First (npEDF) scheduling method is applied to each processor. We did extensive experiments by combining different partitioning strategies with the ordering of tasks (period, utilization, etc). We found that First-Fit Increasing Period, Best-Fit Increasing Period, First-Fit Decreasing Utilization and Best-Fit Decreasing Utilization give reasonably good results. The success ratio of decreasing utilization heuristics are 10%–30% more than increasing period heuristics. We observed that First-Fit Decreasing Utilization and BestFit Decreasing Utilization takes more time than First-Fit Increasing Period and Best-Fit Increasing Period. We also compared our results with the existing approach.
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