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On Computation Offloading and Energy Efficiency on Android Devices
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DOI：10.1109/ICCWORKSHOPS57953.2023.10283704.png)
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Abstract
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As resource-demanding mobile applications become increasingly popular, smartphones, which are resource- and battery-dependent by nature, can migrate their workload to other devices on the Cloud-to-Edge continuum. This is known as computation offloading and allows for the heavy processing to be carried out on another, typically more powerful equipment that prompts the result back to the smartphone. The goal is to improve overall performance, reduce energy consumption, and/or prolong the smartphone's battery life. However, the offloading process might have practical implications, such as performance degradation by increasing the latency of the response time or even increasing the energy consumption of the device if the application requires heavy data transfer. Also, one should consider that the chips in today's smartphones are extremely energy efficient and offer outstanding performance. 5G networks also increase the data transfer bandwidth between devices. We aim to shed light on the circumstances under which computation offloading is a robust architectural solution for mobile apps. We used the Edge-Bench benchmark in our experimental evaluation, namely the audio, image, and scalar applications, over three smartphones. The results highlight different performance and energy consumption depending on the type of device and manufacturer, considering the same application, showing that the offloading decision is not linear, thus it is not always the best solution to minimize battery consumption.
Keywords:
Computation Offloading
EdgeBench
Energy consumption
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