Workload Change Point Detection for Runtime Thermal Management of Embedded Systems
Workload Change Point Detection for Runtime Thermal Management of Embedded Systems
复制标题
用于嵌入式系统运行时热管理的工作负载变化点检测
DOI：
--
复制
发表时间：
2016
影响因子：
2.9
通讯作者：
B. Al
中科院分区：
文献类型：
--
作者：
Anup Das;G. Merrett;M. Tribastone;B. Al
在多核嵌入式系统上执行的应用程序与系统软件 [例如操作系统 (OS)] 和硬件交互，导致热分布变化很大，从而加速某些老化机制，降低使用寿命可靠性。因此，有效管理温度需要：1) 自动检测应用程序工作负载的变化，2) 适当选择控制杆来管理这些工作负载的热分布。在本文中，我们提出了一种使用 CPU 性能统计重叠滑动窗口之间基于密度比的统计散度来检测工作负载变化的技术。这集成在热管理的运行时方法中，该方法使用强化学习通过对板载热传感器进行采样来选择特定于工作负载的热控制杆。确定的控制杆会覆盖操作系统的本机线程分配决策并扩展硬件电压频率，以改善平均温度、峰值温度和热循环。所提出的方法通过其作为 Linux 分层运行时管理器的实现进行了验证，从较高层次结构中选择基于启发式的线程亲和力以减少热循环，并从较低层次结构中选择基于学习的电压频率以降低平均和峰值温度。在基于 ARM 的嵌入式系统上对移动、嵌入式和高性能应用进行的实验表明，与最先进的方法相比，所提出的方法将工作负载变化检测精度平均提高了 3.4 倍，平均温度降低了 4 °C-25 °C，峰值温度降低了 6 °C-24 °C，热循环降低了 7%-35%。
Applications executed on multicore embedded systems interact with system software [such as the operating system (OS)] and hardware, leading to widely varying thermal profiles which accelerate some aging mechanisms, reducing the lifetime reliability. Effectively managing the temperature therefore requires: 1) autonomous detection of changes in application workload and 2) appropriate selection of control levers to manage thermal profiles of these workloads. In this paper, we propose a technique for workload change detection using density ratio-based statistical divergence between overlapping sliding windows of CPU performance statistics. This is integrated in a runtime approach for thermal management, which uses reinforcement learning to select workload-specific thermal control levers by sampling on-board thermal sensors. Identified control levers override the OSs native thread allocation decision and scale hardware voltage-frequency to improve average temperature, peak temperature, and thermal cycling. The proposed approach is validated through its implementation as a hierarchical runtime manager for Linux, with heuristic-based thread affinity selected from the upper hierarchy to reduce thermal cycling and learningbased voltage-frequency selected from the lower hierarchy to reduce average and peak temperatures. Experiments conducted with mobile, embedded, and high performance applications on ARM-based embedded systems demonstrate that the proposed approach increases workload change detection accuracy by an average 3.4×, reducing the average temperature by 4 °C-25 °C, peak temperature by 6 °C-24 °C, and thermal cycling by 7%-35% over state-of-the-art approaches.
改进触发器的状态完整性，以实现 PVT 变化下的电压缩放保留
DOI：
10.1109/tcsi.2013.2252640
发表时间：
2013
期刊：
Regular Papers
影响因子：
--
作者：
Yang S
通讯作者：
Yang S