V-edge: Fast Self-constructive Power Modeling of Smartphones Based on Battery Voltage Dynamics

System power models are important for power management and optimization on smartphones. However, existing approaches for power modeling have several limitations. Some require external power meters, which is not convenient for people to use. Other approaches either rely on the battery current sensing capability, which is not available on many smartphones, or take a long time to generate the power model. To overcome these limitations, we propose a new way of generating power models from battery voltage dynamics, called V-edge. V-edge is self-constructive and does not require current-sensing. Most importantly, it is fast in model building. Our implementation supports both component level power models and per-application energy accounting. Evaluation results using various benchmarks and applications show that the V-edge approach achieves high power modeling accuracy, and is two orders of magnitude faster than existing self-modeling approaches requiring no current-sensing.

[1]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[2]  Carla Schlatter Ellis,et al.  Energy estimation tools for the Palm , 2000, MSWIM '00.

[3]  Hojung Cha,et al.  DevScope: a nonintrusive and online power analysis tool for smartphone hardware components , 2012, CODES+ISSS.

[4]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[5]  Hojung Cha,et al.  AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring , 2012, USENIX Annual Technical Conference.

[6]  Lin Zhong,et al.  Self-constructive high-rate system energy modeling for battery-powered mobile systems , 2011, MobiSys '11.

[7]  Gokhan Memik,et al.  Into the wild: Studying real user activity patterns to guide power optimizations for mobile architectures , 2009, 2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[8]  Paramvir Bahl,et al.  Fine-grained power modeling for smartphones using system call tracing , 2011, EuroSys '11.

[9]  Chandra Krintz,et al.  A run-time, feedback-based energy estimation model For embedded devices , 2006, Proceedings of the 4th International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS '06).

[10]  Lin Zhong,et al.  Chameleon: A Color-Adaptive Web Browser for Mobile OLED Displays , 2012, IEEE Transactions on Mobile Computing.

[11]  Ranveer Chandra,et al.  Empowering developers to estimate app energy consumption , 2012, Mobicom '12.

[12]  R. H. Myers Classical and modern regression with applications , 1986 .

[13]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[14]  Mahadev Satyanarayanan,et al.  PowerScope: a tool for profiling the energy usage of mobile applications , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.