In modern multi-core Mixed-Criticality (MC) systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, specially in the overload situation, may lead to thermal issues, which may affect the reliability and timeliness of MC systems. Therefore, managing peak power consumption has become imperative in multi-core MC systems. In this regard, we propose an online peak power and thermal management heuristic for multi-core MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and per-cluster Dynamic Voltage and Frequency Scaling (DVFS). Specifically, our approach examines multiple tasks ahead to determine the most appropriate one for slack assignment, that has the most impact on the system peak power and temperature. However, changing the frequency and selecting a proper task for slack assignment and a proper core for task re-mapping at runtime can be time-consuming and may cause deadline violation which is not admissible for high-criticality tasks. Therefore, we analyze and then optimize our run-time scheduler and evaluate it for various platforms. The proposed approach is experimentally validated on the ODROID-XU3 (DVFS-enabled heterogeneous multi-core platform) with various embedded real-time benchmarks. Results show that our heuristic achieves up to 5.25% reduction in system peak power and 20.33\% reduction in maximum temperature compared to an existing method while meeting deadline constraints in different criticality modes.

中文翻译：

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多核平台上混合临界系统的功耗感知运行时调度程序

在现代多核混合临界 (MC) 系统中，由于以最大频率并行执行任务而导致峰值功耗上升，特别是在过载情况下，可能会导致热问题，从而影响 MC 的可靠性和及时性系统。因此，管理峰值功耗在多核 MC 系统中变得势在必行。在这方面，我们为多核 MC 系统提出了一种在线峰值功率和热管理启发式方法。这种启发式方法通过利用动态松弛和每个集群的动态电压和频率缩放 (DVFS)，在运行时尽可能地降低系统的峰值功耗。具体来说，我们的方法会提前检查多项任务，以确定最适合松弛分配的任务，这对系统峰值功率和温度影响最大。然而，更改频率并为松弛分配选择合适的任务以及在运行时为任务重新映射选择合适的核心可能非常耗时，并且可能导致违反截止日期，这对于高危任务来说是不可接受的。因此，我们分析并优化我们的运行时调度程序，并针对各种平台对其进行评估。所提出的方法在 ODROID-XU3（支持 DVFS 的异构多核平台）上通过各种嵌入式实时基准进行了实验验证。结果表明，与现有方法相比，我们的启发式方法在满足不同临界模式下的期限限制的同时，系统峰值功率降低了 5.25%，最高温度降低了 20.33%。我们分析并优化我们的运行时调度程序，并针对各种平台对其进行评估。所提出的方法在 ODROID-XU3（支持 DVFS 的异构多核平台）上通过各种嵌入式实时基准进行了实验验证。结果表明，与现有方法相比，我们的启发式方法在满足不同临界模式下的期限限制的同时，系统峰值功率降低了 5.25%，最高温度降低了 20.33%。我们分析并优化我们的运行时调度程序，并针对各种平台对其进行评估。所提出的方法在 ODROID-XU3（支持 DVFS 的异构多核平台）上通过各种嵌入式实时基准进行了实验验证。结果表明，与现有方法相比，我们的启发式方法在满足不同临界模式下的期限限制的同时，系统峰值功率降低了 5.25%，最高温度降低了 20.33%。