Modern mobile devices are equipped with heterogeneous multicore processors which integrate asymmetric CPU cores and GPUs. More cores require additional power consumption and produce more heat, which can result in performance degradation due to thermal throttling. To address this issue, this paper proposes a QT-adaptation engine to monitor current temperature and quality of service (QoS), and derives a QoS-temperature model (QT-model) through a run-time learning mechanism (QT-learning) to balance dynamic workloads and dynamic thermal behavior. Based on the derived QT-model, the QT-adaptation engine migrates threads among cores using the proposed critical thread aware scheduler to ensure high QoS, and uses a self-adapting governor to meet the temperature constraint for system robustness. The concept is implemented on a commercial LG Nexus $5\times$ and evaluated using real world applications. Results show the proposed approach increases the frame per second rate by up to 25% compared to other current methods while meeting temperature constraints.

中文翻译：

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QT 自适应引擎：热约束移动设备中的自适应 QoS 感知调度和管理

现代移动设备配备了集成了非对称 CPU 内核和 GPU 的异构多核处理器。更多的内核需要额外的功耗并产生更多的热量，这可能会因热节流而导致性能下降。为了解决这个问题，本文提出了一个 QT-adaptation 引擎来监控当前的温度和服务质量 (QoS)，并通过运行时学习机制 (QT-learning) 推导出一个 QoS-温度模型 (QT-model) 以平衡动态工作负载和动态热行为。基于派生的 QT 模型，QT 适配引擎使用提出的关键线程感知调度器在内核之间迁移线程以确保高 QoS，并使用自适应调控器来满足系统鲁棒性的温度约束。这个概念是在商业 LG Nexus 上实现的，$5\times$ 并使用现实世界的应用程序进行评估。结果表明，在满足温度限制的同时，与其他当前方法相比，所提出的方法将每秒帧速率提高了 25%。