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Schedulability analysis of DAG tasks with arbitrary deadlines under global fixed-priority scheduling

机译:全球固定优先级计划下具有任意截止日期的DAG任务的可调度性分析

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One of the major sources of pessimism in the response time analysis (RTA) of globally scheduled real-time tasks is the computation of an upper-bound on the inter-task interference. This problem is further exacerbated when intra-task parallelism is permitted because of the complex internal structure of parallel tasks. This paper considers the global fixed-priority (G-FP) scheduling of sporadic real-time tasks when each task is modeled by a directed acyclic graph (DAG) of concurrent subtasks. We present a RTA based on the concept of problem window, a technique that has been extensively used to study the schedulability of sequential task in multiprocessor systems. The problem window approach of RTA usually categorizes interfering jobs in three different groups: carry-in, carry-out and body jobs. In this paper, we propose two novel techniques to derive less pessimistic upper-bounds on the workload produced by the carry-in and carry-out jobs of the interfering tasks. Those new bounds take into account the precedence constraints between subtasks pertaining to the same DAG. We show that with this new characterization of the carry-in and carry-out workload, the proposed schedulability test offers significant improvements on the schedulability of DAG tasks for randomly generated task sets in comparison to state-of-the-art techniques. In fact, we show that, while the state-of-art analysis does not scale with an increasing number of processors when tasks have constrained deadlines, the results of our analysis are barely impacted by the processor count in both the constrained and the arbitrary deadline case.
机译:在全局调度的实时任务的响应时间分析(RTA)中,悲观情绪的主要来源之一是计算任务间干扰的上限。当由于并行任务的内部结构复杂而允许任务内并行时,此问题会进一步恶化。当每个任务由并发子任务的有向无环图(DAG)建模时,本文考虑了零星实时任务的全局固定优先级(G-FP)调度。我们提出基于问题窗口概念的RTA,该技术已被广泛用于研究多处理器系统中顺序任务的可调度性。 RTA的问题窗口方法通常将干扰作业分为三个不同的类别:随身携带,随身携带和身体作业。在本文中,我们提出了两种新颖的技术来减少干扰任务的随身携带和随身携带工作所产生的工作量的悲观上限。这些新界限考虑了与同一DAG相关的子任务之间的优先级约束。我们显示出,利用随身携带和随身携带工作负载的这一新特性,与最新技术相比,建议的可计划性测试对随机生成的任务集的DAG任务的可计划性进行了重大改进。实际上,我们表明,尽管当任务的截止日期受到限制时,最新的分析方法并不能随着处理器数量的增加而扩展,但是在受限和任意期限内,我们的分析结果几乎不受处理器数量的影响案件。

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