by Anthony Canino, Yu David Liu and
Hidehiko MasuharaAbstract:
Mobile applications regularly interact with their noisy and ever-changing physical environment. The fundamentally uncertain nature of such interactions leads to significant challenges in energy optimization, a crucial goal of software engineering on mobile devices. This paper presents Aeneas, a novel energy optimization framework for Android in the presence of uncertainty. Aeneas provides a minimalistic programming model where acceptable program behavioral settings are abstracted as knobs and application-specific optimization goals such as meeting an energy budget are crystallized as rewards, both of which are directly programmable. At its heart, Aeneas is endowed with a stochastic optimizer to adaptively and intelligently select the reward-optimal knob setting through a form of reinforcement learning. We evaluate Aeneas on mobile GPS applications built over Google LocationService API. Through an in-field case study that covers approximately 6500 miles and 150 hours of driving as well as 20 hours of biking and hiking, we find that Aeneas can effectively and resiliently meet programmer-specified energy budgets in uncertain physical environments where individual GPS readings undergo significant fluctuation. Compared with non-stochastic approaches such as profile-guided optimization, Aeneas produces significantly more stable results across runs.
Reference:
Stochastic Energy Optimization for Mobile GPS Applications (Anthony Canino, Yu David Liu and Hidehiko Masuhara), In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM, 2018.
Bibtex Entry:
@inproceedings{canino2018fse,
pdf = {fse2018.pdf},
author = {Anthony Canino and {Yu David} Liu and Hidehiko Masuhara},
title = {Stochastic Energy Optimization for Mobile {GPS} Applications},
booktitle = {Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
series = {ESEC/FSE 2018},
year = 2018,
isbn = {978-1-4503-5573-5},
pages = {703--713},
numpages = {11},
url = {http://doi.acm.org/10.1145/3236024.3236076},
doi = {10.1145/3236024.3236076},
acmid = {3236076},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Energy Management, Stochastic Optimization, Uncertainty},
optannote = {submitted on 2018-03-09, Accepted 2018-06-10},
month = nov,
keywords = {Android},
location = {Lake Buena Vista, Florida, USA},
abstract = {Mobile applications regularly interact with their noisy and ever-changing physical environment. The fundamentally uncertain nature of such interactions leads to significant challenges in energy optimization, a crucial goal of software engineering on mobile devices. This paper presents Aeneas, a novel energy optimization framework for Android in the presence of uncertainty. Aeneas provides a minimalistic programming model where acceptable program behavioral settings are abstracted as knobs and application-specific optimization goals such as meeting an energy budget are crystallized as rewards, both of which are directly programmable. At its heart, Aeneas is endowed with a stochastic optimizer to adaptively and intelligently select the reward-optimal knob setting through a form of reinforcement learning. We evaluate Aeneas on mobile GPS applications built over Google LocationService API. Through an in-field case study that covers approximately 6500 miles and 150 hours of driving as well as 20 hours of biking and hiking, we find that Aeneas can effectively and resiliently meet programmer-specified energy budgets in uncertain physical environments where individual GPS readings undergo significant fluctuation. Compared with non-stochastic approaches such as profile-guided optimization, Aeneas produces significantly more stable results across runs.}
}