Make Smartphones Last A Day: Pre-processing Based Computer Vision Application Offloading

The benefit of offloading applications from smartphones to cloud servers is undermined by the significant energy consumption in data transmission. Most previous approaches attempt to improve the energy efficiency only by choosing a more energy efficient network. However, we find that for computer vision applications, pre-processing the data before offloading can also substantially lower the energy consumption in data transmission at the cost of lower result accuracy. In this paper, we propose a novel online decision making approach to determining the pre-processing level for either higher result accuracy or better energy efficiency in a mobile environment. Different from previous work that maximizes the energy efficiency, our work takes the energy consumption as a constraint. Since people usually charge their smartphones daily, it is unnecessary to extend the battery life to last more than a day. Under both the energy and time constraints, we attempt to solve the problem of maximizing the result accuracy in an online way. Our real-world evaluation shows that the implemented prototype of our approach achieves a near-optimal accuracy for application execution results ( nearly 99% correct detection rate for face detection), and sufficiently satisfies the energy constraint.