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Fine-grained power modeling for smartphones using system call tracing

Published: 10 April 2011 Publication History

Abstract

Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications. We observe that state-of-the-art, utilization-based power modeling correlates the (actual) utilization of a hardware component with its power state, and hence is insufficient in capturing several power behavior not directly related to the component utilization in modern smartphones. Such behavior arise due to various low level power optimizations programmed in the device drivers. We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior. We present the detailed design of such a power modeling scheme and its implementation on Android and Windows Mobile. Our experimental results using a diverse set of applications confirm that the new model significantly improves the fine-grained as well as whole-application energy consumption accuracy. We further demonstrate fine-grained energy accounting enabled by such a fined-grained power model, via amanually implemented eprof, the energy counterpart of the classic gprof tool, for profiling application energy drain.

References

[1]
Android phones steal market share. URL http://bmighty.informationweek.com/mobile/showArticle.jhtml?articleID=224201881.
[2]
Celog event tracking. URL http://msdn.microsoft. com/en-us/library/aa462467.aspx.
[3]
Cyanogenmod: Android community rom based on froyo. URL http://www.cyanogenmod.com/.
[4]
Event tracing for windows (etw). URL http://msdn. microsoft.com/en-us/library/ms751538.aspx.
[5]
Microsoft platform builder. URL http://msdn.microsoft.com/en-us/library/ms938344.aspx.
[6]
Monsoon power monitor. URL http://www.msoon. com/LabEquipment/PowerMonitor/.
[7]
Strace. URL http://linux.die.net/man/1/ strace.
[8]
System tap. URL http://sourceware.org/ systemtap/.
[9]
Windows embedded ce shared source. URL http://msdn.microsoft.com/en-us/windowsembedded/ce/dd567722.aspx.
[10]
Niranjan Balasubramanian, Aruna Balasubramanian, and Arun Venkataramani. Energy consumption in mobile phones: a measurement study and implications for network applications. In Proc of IMC, 2009.
[11]
Paul Barham, Austin Donnelly, Rebecca Isaacs, and Richard Mortier. Using magpie for request extraction and workload modelling. In Proc. of OSDI, 2004.
[12]
F. Bellosa. The benefits of event: driven energy accounting in power-sensitive systems. In Proc. ACM SIGOPS European workshop, 2000.
[13]
W.L. Bircher and L.K. John. Complete system power estimation: A trickle-down approach based on performance events. In Proc. of ISPASS, 2007.
[14]
Abhijit Bose, Xin Hu, Kang G. Shin, and Taejoon Park. Behavioral detection of malware on mobile handsets. In Proc. of MobiSys, 2008.
[15]
Vitaly Chipounov and George Candea. Reverse Engineering of Binary Device Drivers with RevNIC. In Proc. of EuroSys, 2010.
[16]
X. Fan, W.D. Weber, and L.A. Barroso. Power provisioning for a warehouse-sized computer. In Proc. of ISCA, 2007.
[17]
Jason Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications. In Proc. of SOSP, 1999.
[18]
Jason Flinn and M. Satyanarayanan. Powerscope: A tool for profiling the energy usage of mobile applications. In Proc. of WMCSA, 1999.
[19]
S. L. Graham, P. B. Kessler, and M. K. McKusick. gprof: A call graph execution profiler. In Proc. of ACM PLDI, 1982.
[20]
Zhenyu Guo, Xi Wang, Jian Tang, Xuezheng Liu, Zhilei Xu, Ming Wu, M. Frans Kaashoek, and Zheng Zhang. R2: An application-level kernel for record and replay. In OSDI, pages 193--208, 2008.
[21]
A. Kansal, F. Zhao, J. Liu, N. Kothari, and A.A. Bhattacharya. Virtual machine power metering and provisioning. In Proc. of SOCC, 2010.
[22]
A. Mahesri and V. Vardhan. Power consumption breakdown on a modern laptop. Proc. of PACS, 2005.
[23]
F. Rawson. MEMPOWER: A simple memory power analysis tool set. IBM Austin Research Laboratory, 2004.
[24]
Victor Shnayder, Mark Hempstead, Bor rong Chen, Geoff Werner Allen, and Matt Welsh. Simulating the power consumption of large-scale sensor network applications. In Proc. of Sensys, 2004.
[25]
A. Shye, B. Scholbrock, and G. Memik. Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures. In Proc. of MICRO, 2009.
[26]
David C. Snowdon, Etienne Le Sueur, Stefan M. Petters, and Gernot Heiser. Koala: a platform for os-level power management. In Proc. of EuroSys, 2009.
[27]
P. Stanley-Marbell and M. Hsiao. Fast, flexible, cycle-accurate energy estimation. In Proc. of ISLPED, 2001.
[28]
V. Tiwari, S. Malik, A. Wolfe, and M. Tien-Chien Lee. Instruction level power analysis and optimization of software. The Journal of VLSI Signal Processing, 13(2), 1996.
[29]
C Yuan, N Lao, J Wen, J Li, Z Zhang, Y Wang, and W Ma. Automated known problem diagnosis with event traces. In EuroSys, 2006.
[30]
J. Zedlewski, S. Sobti, N. Garg, F. Zheng, A. Krishnamurthy, and R.Wang. Modeling hard-disk power consumption. In Proc. of FAST. USENIX Association, 2003.
[31]
Heng Zeng, Carla S. Ellis, Alvin R. Lebeck, and Amin Vahdat. Ecosystem: Managing energy as a first class operating system resource. In Proc. of ASPLOS, 2002.
[32]
L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R.P. Dick, Z.M. Mao, and L. Yang. Accurate Online Power Estimation and Automatic Battery Behavior Based Power Model Generation for Smartphones. In Proc. of CODES+ISSS, 2010.

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Published In

cover image ACM Conferences
EuroSys '11: Proceedings of the sixth conference on Computer systems
April 2011
370 pages
ISBN:9781450306348
DOI:10.1145/1966445
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 10 April 2011

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Author Tags

  1. energy
  2. mobile
  3. smartphone

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EuroSys '11
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EuroSys '11: Sixth EuroSys Conference 2011
April 10 - 13, 2011
Salzburg, Austria

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EuroSys '11 Paper Acceptance Rate 24 of 161 submissions, 15%;
Overall Acceptance Rate 241 of 1,308 submissions, 18%

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Twentieth European Conference on Computer Systems
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Cited By

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  • (2024)VESTA: Power Modeling with Language Runtime EventsProceedings of the ACM on Programming Languages10.1145/36564028:PLDI(621-646)Online publication date: 20-Jun-2024
  • (2024)SERENUS: Alleviating Low-Battery Anxiety Through Real-time, Accurate, and User-Friendly Energy Consumption Prediction of Mobile ApplicationsProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676437(1-20)Online publication date: 13-Oct-2024
  • (2024)Privacy Leakage in Wireless ChargingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.317306321:2(501-514)Online publication date: Mar-2024
  • (2024)Assessing Predictive Models for Energy Consumption Across Varied Software Environments2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825500(5233-5242)Online publication date: 15-Dec-2024
  • (2024)A Survey on Automatic Source Code Transformation for Green Software GenerationEncyclopedia of Sustainable Technologies10.1016/B978-0-323-90386-8.00122-4(765-779)Online publication date: 2024
  • (2023)LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented RealityIEEE Transactions on Mobile Computing10.1109/TMC.2022.317994322:10(5933-5948)Online publication date: 1-Oct-2023
  • (2023)PyAnaDroid: A fully-customizable execution pipeline for benchmarking Android Applications2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58846.2023.00077(586-591)Online publication date: 1-Oct-2023
  • (2023)GraphPowerNet: Graph-based power consumption profiling for mobile phone applicationsComputer Networks10.1016/j.comnet.2023.110056237(110056)Online publication date: Dec-2023
  • (2023)Design and development of a mobile application for level monitoring of oxygen cylinders using block chain technologyDistributed Computing to Blockchain10.1016/B978-0-323-96146-2.00017-6(415-424)Online publication date: 2023
  • (2023)A large-scale empirical study on mobile performance: energy, run-time and memoryEmpirical Software Engineering10.1007/s10664-023-10391-y29:1Online publication date: 27-Dec-2023
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