目前已有許多研究能夠估計一個程式於本地端執行時所消耗的能源，然而這些方法大都必須倚賴特定硬體元件的資訊，並且無法在多執行緒的環境下準確地估計執行某一支程式所需的能源消耗。本研究提出了一個基於Paired Sampling之方法的能源模型（Power Model），此方法並不倚賴特定的硬體資訊，且能夠於多執行緒的環境下估計執行一支非同步的OSGi Service所需消耗的能源。基於此能源模型，我們同時提出了一個決策模型（Decision Model），能夠動態地做出決策是否要將一支OSGi Service從某一支手機藉由藍芽卸載（Offload）到另一支手機上執行以節省能源。於實驗之中，我們評估了決策的正確性、藉由卸載OSGi Service至遠端手機執行所能節省的能源、以及估計能源的誤差值。

Power models are a critical element in current research regarding the effect of program-offloading decision making on the energy consumption of mobile devices. Several utilization-based power models have been proposed for measuring the energy consumption of locally running programs. However, the main challenge of utilization-based methods is that the models must be retrained for program units that use hardware components that are not addressed in the training phase. This paper proposes a paired sampling-based power model to address this critical challenge. The proposed power model estimates the energy consumption of an OSGi service asynchronously invoked in a multithreading environment on the basis of the overall remaining battery energy information at runtime without pretraining for each specific hardware component. On the basis of the power model, an offloading decision model is proposed to determine whether a service invocation should be offloaded to a nearby mobile device over Bluetooth to conserve energy. The proposed approach was experimentally assessed according to the correctness of decision making, energy gained by offloading service invocations, and weighted absolute percentage error of the estimated energy consumption.

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