Scalable Power Impact Prediction of Mobile Sensing Applications at Pre-Installation Time

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 79
  • Download : 0
Today & x0027;s smartphone application (hereinafter & x2018;app & x2019;) markets do not provide information on power consumption of apps, which is essential for users. Continuous sensing apps make this problem more severe because significant power is consumed without the users & x2019; awareness. We propose <italic>PowerForecaster</italic> to break through such an exhaustive cycle. It provides users with personalized estimation of sensing apps & x0027; power cost at pre-installation time. It is challenging to provide such estimation in advance because the actual power cost of a sensing app varies depending on user behavior such as physical activities and phone use patterns. To address this, we develop a novel <italic>power emulator</italic> as a core component of PowerForecaster. It achieves accurate, personalized power estimation by reproducing users & x0027; behaviors and emulating the target app & x0027;s power use. We optimize the system to make the power emulation fast and its trace collection energy efficient. We further address the problem of dealing with large-scale emulation requests from worldwide deployment. We develop a novel selective emulation approach to minimize the server-side resource cost. We performed extensive experiments and the experimental results show that PowerForecaster achieves the power estimation accuracy of 93.4 percent and saves on 60 percent of the emulator instance usage.
Publisher
IEEE COMPUTER SOC
Issue Date
2019-06
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON MOBILE COMPUTING, v.19, no.6, pp.1448 - 1464

ISSN
1536-1233
DOI
10.1109/TMC.2019.2909897
URI
http://hdl.handle.net/10203/275183
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0