PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time
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 insta...
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2015
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sg-smu-ink.sis_research-41222020-04-07T05:41:25Z PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time MIN, Chulhong LEE, Youngki YOO, Chungkuk KANG, Seungwoo CHOI, Sangwon PARK, Pillsoon HWANG, Inseok JU, Younghyun CHOI, Seungpyo SONG, Junehwa 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. 2015-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3122 info:doi/10.1145/2809695.2809728 https://ink.library.smu.edu.sg/context/sis_research/article/4122/viewcontent/PowerForecaster_2015_pv_oa.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Power impact Sensing applications Pre-installation Smartphone Computer Sciences Software Engineering |
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Power impact Sensing applications Pre-installation Smartphone Computer Sciences Software Engineering MIN, Chulhong LEE, Youngki YOO, Chungkuk KANG, Seungwoo CHOI, Sangwon PARK, Pillsoon HWANG, Inseok JU, Younghyun CHOI, Seungpyo SONG, Junehwa PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time |
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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. |
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MIN, Chulhong LEE, Youngki YOO, Chungkuk KANG, Seungwoo CHOI, Sangwon PARK, Pillsoon HWANG, Inseok JU, Younghyun CHOI, Seungpyo SONG, Junehwa |
author_facet |
MIN, Chulhong LEE, Youngki YOO, Chungkuk KANG, Seungwoo CHOI, Sangwon PARK, Pillsoon HWANG, Inseok JU, Younghyun CHOI, Seungpyo SONG, Junehwa |
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MIN, Chulhong |
title |
PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time |
title_short |
PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time |
title_full |
PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time |
title_fullStr |
PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time |
title_full_unstemmed |
PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time |
title_sort |
powerforecaster: predicting smartphone power impact of continuous sensing applications at pre-installation time |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2015 |
url |
https://ink.library.smu.edu.sg/sis_research/3122 https://ink.library.smu.edu.sg/context/sis_research/article/4122/viewcontent/PowerForecaster_2015_pv_oa.pdf |
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