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FFOB: efficient online mode-switch procrastination in mixed-criticality systems
Real-Time Systems ( IF 1.4 ) Pub Date : 2018-12-01 , DOI: 10.1007/s11241-018-9323-x
Biao Hu , Lothar Thiele , Pengcheng Huang , Kai Huang , Christoph Griesbeck , Alois Knoll

In mixed-criticality scheduling, the widely adopted mode-switch scheme assumes that both high- and low-criticality tasks are schedulable when no tasks overrun (normal mode) and all high-criticality tasks are schedulable even when they overrun (critical mode, where low-criticality tasks are abandoned/degraded). However, this scheme greatly impedes the system performance because triggering a mode-switch immediately after any task overruns is abrupt and pessimistic. It is urgent to solve this problem as the mode-switch scheme is a fundamental part in the mixed-criticality scheduling. In this article, we present an on-the-fly fast overrun budgeting mechanism for both, earliest-deadline-first scheduled and fixed-priority scheduled systems that can effectively keep the system “away” from the critical mode. Our main idea is to perform overrun budgeting for all tasks as a whole, by monitoring task executions and updating a common overrun budget. This way, the overrun budget is shared among all tasks and adaptively replenished by leveraging run-time information; consequently, mode-switch can be postponed as much as possible. Both, extensive simulations and real-life deployments demonstrate that our proposed mode-switch scheme is lightweight and significantly outperforms existing solutions in improving the system’s quality of service for low-criticality tasks.

中文翻译:

FFOB:混合临界系统中的高效在线模式切换拖延

在混合临界调度中,广泛采用的模式切换方案假设高临界任务和低临界任务在没有任务溢出(正常模式)时都是可调度的,并且所有高临界任务即使在它们溢出时也是可调度的(临界模式,其中低关键性任务被放弃/降级)。然而,这种方案极大地阻碍了系统性能,因为在任何任务超限后立即触发模式切换是突然和悲观的。由于模式切换方案是混合临界调度中的基本部分,因此迫切需要解决这个问题。在本文中,我们为最早截止日期优先调度系统和固定优先级调度系统提供了一种即时快速超支预算机制,可以有效地使系统“远离”临界模式。我们的主要思想是通过监控任务执行并更新共同的超支预算,对所有任务执行超支预算作为一个整体。通过这种方式,超支预算在所有任务之间共享,并通过利用运行时信息自适应地补充;因此,可以尽可能推迟模式切换。广泛的模拟和现实生活中的部署都表明,我们提出的模式切换方案是轻量级的,并且在提高系统对低关键任务的服务质量方面明显优于现有解决方案。
更新日期:2018-12-01
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