QT-adaptation Engine: Adaptive QoS-aware Scheduling and Governing in Thermally Constrained Mobile Devices

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.