Software-Based Energy Profiling of Android Apps: Simple, Efficient and Reliable?
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The document discusses software-based energy profiling of Android applications, highlighting the lack of tools for efficiently measuring energy consumption and user complaints about high energy usage affecting app ratings. It introduces the Petra power estimation tool, which estimates energy consumption with good accuracy without requiring specialized hardware. The document notes empirical evaluations demonstrating that 72% of apps have a mean magnitude relative error (MMRE) within 0.01, suggesting significant potential for improving energy estimation techniques in mobile app development.
Software-Based Energy Profiling of Android Apps: Simple, Efficient and Reliable?
- 1. Software-Based Energy Profiling of Android Apps Simple, Efficient and Reliable? Andrea De LuciaAnnibale Panichella Dario Di Nucci Fabio Palomba Andy Zaidman Antonio Prota
- 2. Number of smartphone users worldwide from 2014 to 2020 (in billions) The statistics portal association.
- 3. IDC. Top 10 Smartphone Purchase Drivers. 2014. IDC's ConsumerScape 360. Top 10 Smartphone Purchase Drivers Battery Life 56% 49% 53% Ease of Use 33% 39% 38% Operating System 37% 32% 40% Touch Screen 34% 34% 37% Screen Size 37% 22% 34%
- 4. Users complain about energy consumption of their apps. Energy consumption affects user ratings on app stores. Commercial apps do not have less problems than freely available applications. Wilke et al. Energy consumption and efficiency in mobile applications: A user feedback study. 2013. IEEE International Conference on Green Computing.
- 5. “(The faulty batteries were made) because we needed higher capacity batteries for the Note 7” Koh Dong-jin Samsung’s mobile business chief on Samsung Note 7 battery issue
- 6. “There is growing consensus that advances in battery technology and low-power circuit design cannot, by themselves, meet the energy needs of future mobile computers” Flinn and Satyanarayanan Flinn and Satyanarayanan, Energy-aware adaptation for mobile applications. 1999. ACM Symposium on Operating Systems Principles.
- 8. Lack of tools for quickly and efficiently measure the energy consumption of mobile applications Harman et al. Achievements, open problems and challenges for search based software testing. 2015. IEEE International Conference on Software Testing
- 9. Hardware-based tools Model-based tools Software-based tools “Can SW-based tools lead to measurements close to HW-based ones without any cost overhead?” Lack of tools for quickly and efficiently measure the energy consumption of mobile applications
- 10. Model-based tools Software-based tools “Can SW-based tools lead to measurements close to HW-based ones without any cost overhead?” Lack of tools for quickly and efficiently measure the energy consumption of mobile applications Hardware-based tools + Best precision - Require specialized HW and people - Sample frequency problem
- 11. Hardware-based tools Model-based tools Software-based tools + Best precision - Require specialized HW and people - Sample frequency problem “Can SW-based tools lead to measurements close to HW-based ones without any cost overhead?” + Not require HW - Less precise - Need careful parameters calibration Lack of tools for quickly and efficiently measure the energy consumption of mobile applications
- 12. Hardware-based tools Model-based tools Software-based tools + Best precision - Require specialized HW and people - Sample frequency problem + Not require HW - Less precise - Hawthorne effect “Can SW-based tools lead to measurements close to HW-based ones without any cost overhead?” + Not require HW - Less precise - Need careful parameters calibration Lack of tools for quickly and efficiently measure the energy consumption of mobile applications
- 13. PETrA Power Estimation Tool for Android
- 14. PETrA Power Estimation Tool for Android Based on Project Volta Self-Modeling Paradigm* Method Level Granularity Minimize Hawthorne Effect Strong Integration with Android OS Does not require any specialized HW *Dong and Zhong. Self-constructive high-rate system energy modeling for battery-powered mobile systems. 2011. ACM International Conference on Mobile Systems, Applications, and Services.
- 15. PETrA Workflow Install App Clear Environment Exercise App Compute Energy Consumptions Save Results More runs to perform? Uninstall App
- 16. Smartphone Components Consumption Info Powerprofile file Smartphone Components State during a Time Frame PETrA Energy profile computation Systrace Batterystats Active Methods during a Time Frame dmtracedump Energy Consumption for each Method Call
- 18. How close are the estimations from PETrA to a hardware-based tool? Empirical Evaluation RQ 54apps* Linares-Vasquez et al. Mining energy-greedy api usage patterns in android apps: An empirical study. 2014. Working Conference on Mining Software Repositories. Monsoon Toolkit* 414.899 API calls* 321 APIs* Context selection
- 19. Empirical Evaluation *Linares-Vasquez et al. Mining energy-greedy api usage patterns in android apps: An empirical study. 2014. Working Conference on Mining Software Repositories. Test Environment Setup LG Nexus 4* Monkeyrunner* Data Analysis Metrics 10runs
- 20. Empirical Evaluation *Linares-Vasquez et al. Mining energy-greedy api usage patterns in android apps: An empirical study. 2014. Working Conference on Mining Software Repositories. Test Environment Setup LG Nexus 4* Monkeyrunner* Data Analysis Metrics 10runs Mean Magnitude Relative Error MMRE
- 21. Empirical Evaluation *Linares-Vasquez et al. Mining energy-greedy api usage patterns in android apps: An empirical study. 2014. Working Conference on Mining Software Repositories. Test Environment Setup LG Nexus 4* Monkeyrunner* Data Analysis Metrics 10runs Mean Magnitude Relative Error MMRE Relative Error Deviation within x PRED(x)
- 22. Empirical Evaluation *Linares-Vasquez et al. Mining energy-greedy api usage patterns in android apps: An empirical study. 2014. Working Conference on Mining Software Repositories. Test Environment Setup LG Nexus 4* Monkeyrunner* Data Analysis Metrics 10runs Mean Magnitude Relative Error MMRE Relative Error Deviation within x PRED(x) Ratio under/over estimations
- 23. Results In 72% of apps MMRE is within 0.01. In the worst case MMRE is 0.04 95% of method consumption estimations are within 5% of error.
- 24. Results 89% of estimations are overestimations 11% are underestimations accumulated noise due to network usage strong usage of sensors
- 25. Conclusioni
- 26. Conclusioni
- 27. Conclusioni
- 28. Conclusioni
- 29. Conclusioni
- 30. Conclusioni
- 31. Future works Replicate on larger set of apps including not APIs methods.
- 32. Future works Develop new techniques for providing better estimations. Replicate on larger set of apps including not APIs methods.
- 33. Dario Di Nucci University of Salerno ddinucci@unisa.it http://www.sesa.unisa.it/people/ddinucci/ Thanks for your attention! Questions?
- 34. To what extent developers can handle energy related development issues?