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Global scheduling based reliability-aware power management for multiprocessor real-time systems

机译:基于全局调度的多处理器实时系统基于可靠性的电源管理

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Reliability-aware power management (RAPM) has been a recent research focus due to the negative effects of the popular power management technique dynamic voltage and frequency scaling (DVFS) on system reliability. As a result, several RAPM schemes have been studied for uniprocessor real-time systems. In this paper, for a set of frame-based independent real-time tasks running on multiprocessor systems, we study global scheduling based RAPM (G-RAPM) schemes. Depending on how recovery blocks are scheduled and utilized, both individual-recovery and shared-recovery based G-RAPM schemes are investigated. An important dimension of the G-RAPM problem is how to select the appropriate subset of tasks for energy and reliability management (i.e., scale down their executions while ensuring that they can be recovered from transient faults). We show that making such decision optimally (i.e., the static G-RAPM problem) is NP-hard. Then, for the individual-recovery based approach, we study two efficient heuristics, which rely on local and global task selections, respectively. For the shared-recovery based approach, a linear search based scheme is proposed. The schemes are shown to guarantee the timing constraints. Moreover, to reclaim the dynamic slack generated at runtime from early completion of tasks and unused recoveries, we also propose online G-RAPM schemes which exploit the slack-sharing idea studied in previous work. The proposed schemes are evaluated through extensive simulations. The results show the effectiveness of the proposed schemes in yielding energy savings while simultaneously preserving system reliability and timing constraints. For the static version of the problem, the shared-recovery based scheme is shown to provide better energy savings compared to the individual-recovery based scheme, in virtue of its ability to leave more slack for DVFS. Moreover, by reclaiming the dynamic slack generated at runtime, online G-RAPM schemes are shown to yield better energy savings.
机译:由于流行的电源管理技术动态电压和频率缩放(DVFS)对系统可靠性的负面影响,可靠性感知电源管理(RAPM)成为了最近的研究重点。结果,已经针对单处理器实时系统研究了几种RAPM方案。在本文中,对于在多处理器系统上运行的一组基于帧的独立实时任务,我们研究了基于全局调度的RAPM(G-RAPM)方案。根据恢复块的计划和利用方式,研究了基于个人恢复和共享恢复的G-RAPM方案。 G-RAPM问题的一个重要方面是如何为能源和可靠性管理选择合适的任务子集(即缩减执行量,同时确保可以从瞬态故障中恢复它们)。我们显示出最佳地做出这样的决策(即静态G-RAPM问题)是NP难的。然后,对于基于个人恢复的方法,我们研究了两种有效的启发式方法,分别依赖于本地和全局任务选择。对于基于共享恢复的方法,提出了一种基于线性搜索的方案。示出方案以保证时序约束。此外,为了收回在运行时因任务的早期完成和未使用的恢复而产生的动态松弛,我们还提出了在线G-RAPM方案,该方案利用了先前工作中研究的松弛共享思想。通过广泛的仿真评估了所提出的方案。结果表明,所提方案在节省能源的同时保持系统可靠性和时序约束的有效性。对于该问题的静态版本,与基于个人恢复的方案相比,基于共享恢复的方案由于能够为DVFS留出更多的余地,因此可以提供更好的节能效果。此外,通过回收运行时产生的动态余量,在线G-RAPM方案显示出更好的节能效果。

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