论文标题
多临界系统的多速率流体调度
Multi-Rate Fluid Scheduling of Mixed-Criticality Systems on Multiprocessors
论文作者
论文摘要
在本文中,我们考虑了在同质多处理器平台上隐式 - deadline零星任务系统的混合批判性(MC)计划的问题。过去曾提出了基于流体调度模型的算法,专注于双重临界系统。这些算法根据系统模式使用双速率执行模型。一旦系统切换到高批判性模式,此类任务的执行率就会提高以满足其需求的增加。尽管这些算法是最佳的加快,但它们无法安排几个可行的双重临界任务系统。这是因为在模式开关之后,每个高批判性任务的单个固定执行率无法有效地处理模式开关后的过渡期间的高可变性。只要高批判性任务(即模式转换之前)释放的工作尚未完成,就存在这种需求的可变性。在解决这一缺点时,我们在本文中为双重临界任务系统提出了一个多速率流体执行模型。在此模型下,高批判性任务是在模式开关后的过渡期内分配了不同的执行率,以有效处理需求变异性。我们为提出的模型提供了足够的可调度测试,并显示了其对双率流体执行模型的优势。此外,我们还为多速率模型提出了加快最佳速率分配策略,并在实验上表明,所提出的模型的表现优于所有现有的MC调度算法,并具有已知的加速范围。
In this paper we consider the problem of mixed-criticality (MC) scheduling of implicit-deadline sporadic task systems on a homogenous multiprocessor platform. Focusing on dual-criticality systems, algorithms based on the fluid scheduling model have been proposed in the past. These algorithms use a dual-rate execution model for each high-criticality task depending on the system mode. Once the system switches to the high-criticality mode, the execution rates of such tasks are increased to meet their increased demand. Although these algorithms are speed-up optimal, they are unable to schedule several feasible dual-criticality task systems. This is because a single fixed execution rate for each high-criticality task after the mode switch is not efficient to handle the high variability in demand during the transition period immediately following the mode switch. This demand variability exists as long as the carry-over jobs of high-criticality tasks, that is jobs released before the mode switch, have not completed. Addressing this shortcoming, we propose a multi-rate fluid execution model for dual-criticality task systems in this paper. Under this model, high-criticality tasks are allocated varying execution rates in the transition period after the mode switch to efficiently handle the demand variability. We derive a sufficient schedulability test for the proposed model and show its dominance over the dual-rate fluid execution model. Further, we also present a speed-up optimal rate assignment strategy for the multi-rate model, and experimentally show that the proposed model outperforms all the existing MC scheduling algorithms with known speed-up bounds.