為處理日益複雜的應用程式，現代行動裝置多配置非對稱的中央處理器(CPU)及圖型處理器(GPU)。然而，熱能議題隨著增加的處理器數目與其運算能力變得日漸嚴重。現代的行動裝置為了確保處理器的溫度在安全的範圍內，通常使用較為保守的熱管理，使得應用程式之服務品質在過熱狀況下大幅降低。因此，在這篇論文中，我們提出QT-adaption機制，用來幫助系統在溫度限制下提供較好的服務品質。QT-adaption機制會於系統運行階段偵測系統溫度及服務品質，動態建置熱服務品質模型，進而利用該模型動態決定執行緒運行的處理器、各處理器的執行頻率、以及開啟的處理器數目，取得系統溫度及服務品質的平衡點。因應系統的動態熱行為，熱服務品質模型也會利用QT-adaption於運行時間自我修正，動態適應當下系統情況。QT-adaption機制實做於LG Nexus 5x手機上，實驗結果顯示該演算法能在熱限制下最佳化服務品質。

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-adaption engine to monitor current temperature and QoS, and derive a performance and thermal 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-adaption engine migrates threads among cores using the proposed CT-aware scheduler to ensure high QoS, and uses a self-adaption governor to meet the temperature constraint for system robustness. The concept is implemented on a commercial LG Nexus 5x and evaluated using real world applications. Results show the proposed approach can improve QoS by up to 35% FPS compared to other current methods while meeting temperature constraints.

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