论文标题
基于差异的混合批判性系统的分区调度
Utilization Difference Based Partitioned Scheduling of Mixed-Criticality Systems
论文作者
论文摘要
混合批评性（MC）系统将多个功能与不同的批判性合并到单个硬件平台上。这样的系统改善了总体资源利用，同时保证资源到关键任务。在本文中，我们专注于分区多处理器MC计划的问题，特别是设计有效分区策略的问题。我们基于均匀分配总高临界利用率和所有处理器关键任务的总低临界利用率之间的差异的原理制定了两种新的分区策略。通过平衡这种差异，我们能够减少在每个处理器上应用的UniproCessor MC可调度性测试中的悲观情绪，从而提高了整体调度性。为了评估提出的策略的可计划性能，我们将它们与现有的分区算法进行了比较。我们表明，提出的策略是最早的动态优先级截止日期（EDF-VD）和固定优先级自适应混合临界（AMC）算法，这是有效的。具体而言，我们的结果表明，对于隐式和受约束的deadline任务系统，提出的策略分别提高了28.1％和36.2％。
Mixed-Criticality (MC) systems consolidate multiple functionalities with different criticalities onto a single hardware platform. Such systems improve the overall resource utilization while guaranteeing resources to critical tasks. In this paper, we focus on the problem of partitioned multiprocessor MC scheduling, in particular the problem of designing efficient partitioning strategies. We develop two new partitioning strategies based on the principle of evenly distributing the difference between total high-critical utilization and total low-critical utilization for the critical tasks among all processors. By balancing this difference, we are able to reduce the pessimism in uniprocessor MC schedulability tests that are applied on each processor, thus improving overall schedulability. To evaluate the schedulability performance of the proposed strategies, we compare them against existing partitioned algorithms using extensive experiments. We show that the proposed strategies are effective with both dynamic-priority Earliest Deadline First with Virtual Deadlines (EDF-VD) and fixed-priority Adaptive Mixed-Criticality (AMC) algorithms. Specifically, our results show that the proposed strategies improve schedulability by as much as 28.1% and 36.2% for implicit and constrained-deadline task systems respectively.
