PowerForecaster: Predicting power impact of mobile sensing applications at pre-installation time

• Main Authors: MIN, Chulhong, LEE, Youngki, YOO, Chungkuk, KANG, Seungwoo, HWANG, Inseok, SONG, Junehwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Power; Estimation; System; Sensing; Pre-installation tie; Computer Sciences; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3307; https://ink.library.smu.edu.sg/context/sis_research/article/4309/viewcontent/powerforecaster.pdf
• Institution: Singapore Management University

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe that the time for active sensing and processing of an app can vary up to three times with 27 people's sensor traces collected over three weeks. PowerForecaster adopts a novel power emulator that emulates the power use of a sensing app while reproducing users' physical activities and phone use patterns, achieving accurate, personalized power estimation. Our experiments with three commercial apps and two research prototypes show that PowerForecaster achieves 93.4% accuracy under 20 use cases. Also, we optimize the system to accelerate emulation speed and reduce overheads, and show the effectiveness of such optimization techniques.