Estimating mobile application energy consumption using program analysis
Shuai Hao, Ding Li, William G. J. Halfond, Ramesh Govindan
Abstract
Shuai Hao, Ding Li, William G. J. Halfond, Ramesh Govindan
Abstract
Abstract—Optimizing the energy efficiency of mobile applica-tions can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior. Index Terms—Mobile app, fine-grained energy estimation, program analysis. I.
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Abstract—Optimizing the energy efficiency of mobile applica-tions can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior. Index Terms—Mobile app, fine-grained energy estimation, program analysis. I.
Key concepts: Energy consumption, Computer science, Energy (signal processing), Energy modeling, Efficient energy use, Set (abstract data type), Consumption (sociology), Code (set theory)