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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Main Authors: MIN, Chulhong, LEE, Youngki, YOO, Chungkuk, KANG, Seungwoo, CHOI, Sangwon, PARK, Pillsoon, HWANG, Inseok, JU, Younghyun, CHOI, Seungpyo, SONG, Junehwa
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access: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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Institution: Singapore Management University
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spelling 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Power impact
Sensing applications
Pre-installation
Smartphone
Computer Sciences
Software Engineering
spellingShingle 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
description 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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author 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
author_sort 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
_version_ 1770572817946902528