Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures

As the market for mobile architectures continues its rapid growth, it has become increasingly important to understand and optimize the power consumption of these battery-driven devices. While energy consumption has been heavily explored, there is one critical factor that is often overlooked -- the end user. Ultimately, the energy consumption of a mobile architecture is defined by user activity. In this paper, we study mobile architectures in their natural environment -- in the hands of the end user. Specifically, we develop a logger application for Android G1 mobile phones and release the logger into the wild to collect traces of real user activity. We then show how the traces can be used to characterize power consumption, and guide the development of power optimizations.

We present a regression-based power estimation model that only relies on easily-accessible measurements collected by our logger. The model accurately estimates power consumption and provides insights about the power breakdown among hardware components. We show that energy consumption widely varies depending upon the user. In addition, our results show that the screen and the CPU are the two largest power consuming components. We also study patterns in user behavior to derive power optimizations. We observe that majority of the active screen time is dominated by long screen intervals. To reduce the energy consumption during these long intervals, we implement a scheme that slowly reduces the screen brightness over time. Our results reveal that the users are happier with a system that slowly reduces the screen brightness rather than abruptly doing so, even though the two schemes settle at the same brightness. Similarly, we experiment with a scheme that slowly reduces the CPU frequency over time. We evaluate these optimizations with a user study and demonstrate 10.6% total system energy savings with a minimal impact on user satisfaction.