Performance Assessment of Dynamic Analysis Based Energy Estimation Tools

Recently, the preference of users shifted the computational platform to resource constrained smartphone devices as users prefer to work while on the go. The shift of information access paradigm on smartphone devices demands high functionality applications to enrich user experience that as a result surges battery’s charge consumption rate. Energy estimation schemes consider smartphone component’s power measurement or code analysis methods for energy estimation of smartphone applications. This work assesses the performance overhead of existing dynamic analysis based energy estimation tools. It investigates estimation time, energy overhead, and resources consumption rate of existing estimation tools. It also analyzes the performance overhead of simulation/emulation based application profiling methods for smartphone applications.

[1]  Xuanzhe Liu,et al.  Predicting Smartphone Battery Life based on Comprehensive and Real-time Usage Data , 2018, ArXiv.

[2]  Nathan Ickes,et al.  Instruction level and operating system profiling for energy exposed software , 2003, IEEE Trans. Very Large Scale Integr. Syst..

[3]  Raja Wasim Ahmad,et al.  Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices , 2017, FNC/MobiSPC.

[4]  David Pichardie,et al.  Formal Verification of Loop Bound Estimation for WCET Analysis , 2013, VSTTE.

[5]  Olivier Philippot,et al.  Software Measurement of Energy Consumption on Smartphones , 2018, Greening Video Distribution Networks.

[6]  Mary Jane Irwin,et al.  Instruction level power profiling , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

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

[8]  Matti Siekkinen,et al.  Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices , 2015, ACM Comput. Surv..

[9]  Mickaël Raulet,et al.  Energy estimation models for video decoders: reconfigurable video coding-CAL case-study , 2015, IET Comput. Digit. Tech..

[10]  Joel J. P. C. Rodrigues,et al.  A case and framework for code analysis-based smartphone application energy estimation , 2017, Int. J. Commun. Syst..

[11]  Joel J. P. C. Rodrigues,et al.  A survey on energy estimation and power modeling schemes for smartphone applications , 2017, Int. J. Commun. Syst..

[12]  Lin Zhong,et al.  Demo: sesame: self-constructive system energy modeling for battery-powered mobile systems , 2011, MobiSys '11.

[13]  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).