An Environment for Automated Measuring of Energy Consumed by Android Mobile Devices

Armen Dzhagaryan, Aleksandar Milenković, Mladen Milosevic, Emil Jovanov

Abstract

Mobile devices such as smartphones, tablets, and e-readers have become the dominant type of computing platforms. Energy-efficiency has become a key design and operating requirement for applications running on mobile devices. It is further underscored by a growing reliance of consumers on services delivered through mobile devices and their growing complexity and sophistication. A detailed measurement-based characterization of energy needs of mobile applications is important for both device manufacturers and application developers, as it may identify energy-demanding activities and guide optimizations. In this paper, we describe an environment for automated energy measurements of applications running on Android mobile devices. We discuss hardware and software aspects of the environment and several approaches to runtime capturing and timestamping of activities of interest. Finally, we demonstrate the use of the environment in several case studies conducted on Google’s Nexus 4 smartphone.

References

  1. Bircher, W.L., John, L.K., 2012. Complete System Power Estimation Using Processor Performance Events. IEEE Trans. Comput. 61, 563 -577. doi:10.1109/TC.2011.47.
  2. Carroll, A., Heiser, G., 2010. An analysis of power consumption in a smartphone, in: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, USENIXATC'10. USENIX Association, Berkeley, CA, USA, pp. 21-21.
  3. Cisco, 2016. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015-2020 White Paper [WWW Document]. Cisco. URL http://cisco.com/c/en/us/solutions/collateral/serviceprovider/visual-networking-index-vni/mobile-whitepaper-c11-520862.html (accessed 2.13.16).
  4. CyanogenMod, 2014. CyanogenMod | Android Community Operating System [WWW Document]. URL http://www.cyanogenmod.org/ (accessed 6.14.14).
  5. Gartner, Inc., 2016. Worldwide Device Shipments to Grow 1.9 Percent in 2016, While End-User Spending to Decline for the First Time [WWW Document]. URL http://www.gartner.com/newsroom/id/3187134 (accessed 2.13.16).
  6. Google, 2015. Android Debug Bridge | Android Developers [WWW Document]. URL http://developer.android.com/tools/help/adb.html (accessed 6.14.15).
  7. Google, 2014a. Nexus - Google [WWW Document]. URL http://www.google.com/intl/all/nexus (accessed 6.15.14).
  8. Google, 2014b. Android [WWW Document]. URL http://www.android.com/ (accessed 6.20.14).
  9. Google, 2014c. Log | Android Developers [WWW Document]. URL http://developer.android.com/ reference/android/util/Log.html (accessed 8.3.14).
  10. IDC, 2016. Apple, Huawei, and Xiaomi Finish 2015 with Above Average Year-Over-Year Growth, as Worldwide Smartphone Shipments Surpass 1.4 Billion for the Year, According to IDC [WWW Document]. www.idc.com. URL http://www.idc.com/getdoc.jsp? containerId=prUS40980416 (accessed 2.13.16).
  11. Li, T., John, L.K., 2003. Run-time modeling and estimation of operating system power consumption. SIGMETRICS Perform Eval Rev 31, 160-171. doi:10.1145/885651.781048.
  12. Milenkovic, A., Dzhagaryan, A., Burtscher, M., 2013. Performance and Energy Consumption of Lossless Compression/Decompression Utilities on Mobile Computing Platforms, in: Modeling, Analysis Simulation of Computer and Telecommunication Systems (MASCOTS), 2013 IEEE 21st International Symposium on. pp. 254-263. doi:10.1109/MASCOTS.2013.33.
  13. Milenkovic, A., Milenkovic, M., Jovanov, E., Hite, D., Raskovic, D., 2005. An environment for runtime power monitoring of wireless sensor network platforms, in: System Theory, 2005. SSST'05. Proceedings of the Thirty-Seventh Southeastern Symposium on. pp. 406-410.
  14. Milosevic, M., Dzhagaryan, A., Jovanov, E., Milenkovic, A., 2013. An Environment for Automated Power Measurements on Mobile Computing Platforms, in: Proceedings of the 51st ACM Southeast Conference, ACMSE 7813. ACM, New York, NY, USA, p. 6. doi:10.1145/2498328.2500064.
  15. NI, 2014a. NI PXIe-4154 - National Instruments [WWW Document]. URL http://sine.ni.com/nips/cds/view/p/lang/en/nid/209090 (accessed 6.20.14).
  16. NI, 2014b. NI PXIe-1073 - National Instruments [WWW Document]. URL http://sine.ni.com/nips/cds/view/ p/lang/en/nid/207401 (accessed 6.20.14).
  17. Pathak, A., Hu, Y.C., Zhang, M., 2012. Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof, in: Proceedings of the 7th ACM European Conference on Computer Systems, EuroSys 7812. ACM, New York, NY, USA, pp. 29-42. doi:10.1145/2168836.2168841.
  18. Pathak, A., Hu, Y.C., Zhang, M., Bahl, P., Wang, Y.-M., 2011. Fine-grained power modeling for smartphones using system call tracing, in: Proceedings of the Sixth Conference on Computer Systems, EuroSys 7811. ACM, New York, NY, USA, pp. 153-168. doi:10.1145/1966445.1966460.
  19. Qualcomm, 2014. SnapdragonTM Mobile Processors - Qualcomm Developer Network [WWW Document]. URL https://developer.qualcomm.com/discover/ chipsets-and-modems/snapdragon (accessed 6.20.14).
  20. Rice, A., Hay, S., 2010a. Measuring mobile phone energy consumption for 802.11 wireless networking. Pervasive Mob. Comput., Special Issue PerCom 2010 6, 593-606. doi:10.1016/j.pmcj.2010.07.005.
  21. Rice, A., Hay, S., 2010b. Decomposing power measurements for mobile devices, in: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom). Presented at the 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 70- 78. doi:10.1109/PERCOM.2010.5466991.
  22. Shye, A., Scholbrock, B., Memik, G., 2009. Into the wild: Studying real user activity patterns to guide power optimizations for mobile architectures, in: 42nd Annual IEEE/ACM International Symposium on Microarchitecture, 2009. MICRO-42. Presented at the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, 2009. MICRO-42, pp. 168-178.
Download


Paper Citation


in Harvard Style

Dzhagaryan A., Milenković A., Milosevic M. and Jovanov E. (2016). An Environment for Automated Measuring of Energy Consumed by Android Mobile Devices . In Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, (PECCS 2016) ISBN 978-989-758-195-3, pages 28-39. DOI: 10.5220/0005950800280039


in Bibtex Style

@conference{pec16,
author={Armen Dzhagaryan and Aleksandar Milenković and Mladen Milosevic and Emil Jovanov},
title={An Environment for Automated Measuring of Energy Consumed by Android Mobile Devices},
booktitle={Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, (PECCS 2016)},
year={2016},
pages={28-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005950800280039},
isbn={978-989-758-195-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, (PECCS 2016)
TI - An Environment for Automated Measuring of Energy Consumed by Android Mobile Devices
SN - 978-989-758-195-3
AU - Dzhagaryan A.
AU - Milenković A.
AU - Milosevic M.
AU - Jovanov E.
PY - 2016
SP - 28
EP - 39
DO - 10.5220/0005950800280039