Power Estimation for Mobile Applications with Profile-Driven Battery Traces

Abstract

It becomes very important to understand power characteristics of mobile applications because more and more complex applications are running on modern smartphones. Although many techniques have been proposed to estimate the power dissipation rate for mobile applications, it typically requires hardware support (i.e., power meters) or complex power models (software profiling or hardware parameters). These techniques might work well in labs with a small set of applications. However, it becomes impractical when we try to estimate the power of mobile applications in an uncontrolled environment. This paper proposes a novel method for estimating the power consumption of mobile applications with profile-based battery traces. Battery traces can be easily collected through a user-level application on any devices. Although it is difficult to achieve accurate results for only a few users because battery changes are coarse-grained, the method is expected to reach an accurate estimation when the number of battery traces reaches a certain scale. Our experiments based on battery traces from more than 80,000 users demonstrate that it is possible to estimate application power with only coarse-grained battery traces. The results are also validated with measured power numbers from a Monsoon power monitor.