Reference : Software-Based Energy Profiling of Android Apps: Simple, Efficient and Reliable?
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
Software-Based Energy Profiling of Android Apps: Simple, Efficient and Reliable?
Di Nucci, Dario mailto [University of Salerno > Computer Science]
Palomba, Fabio mailto [University of Salerno > Computer Science]
Prota, Antonio [University of Salerno > Computer Science]
Panichella, Annibale mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Zaidman, Andy mailto [Delft University of Technology > EWI]
De Lucia, Andrea mailto [University of Salerno > Computer Science]
Proceedings of the 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2017)
International Conference on Software Analysis, Evolution, and Reengineering
from 21-02-2017 to 24-02-2016
[en] Energy Consumption ; Mobile Apps ; Estimation
[en] Modeling the power profile of mobile applications is a crucial activity to identify the causes behind energy leaks. To this aim, researchers have proposed hardware-based tools as well as model-based and software-based techniques to approximate the actual energy profile. However, all these solutions present their own advantages and disadvantages. Hardware-based tools are highly precise, but at the same time their use is bound to the acquisition of costly hardware components. Model-based tools require the calibration of parameters needed to correctly create a model on a specific hardware device. Software-based approaches do not need any hardware components, but they rely on battery measurements and, thus, they are hardware-assisted. These tools are cheaper and easier to use than hardware-based tools, but they are believed to be less precise. In this paper, we take a deeper look at the pros and cons of software-based solutions investigating to what extent their measurements depart from hardware-based solutions. To this aim, we propose a software-based tool named PETRA that we compare with the hardware-based MONSOON toolkit on 54 Android apps. The results show that PETRA performs similarly to MONSOON despite not using any sophisticated hardware components. In fact, in all the apps the mean relative error with respect to MONSOON is lower than 0.05. Moreover, for 95% of the analyzed methods the estimation error is within 5% of the actual values measured using the hardware-based toolkit.
Researchers ; Professionals

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