Workload Change Point Detection for Runtime Thermal Management of Embedded Systems.

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 learning-based 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\times , 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. [ABSTRACT FROM AUTHOR]