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A Fine-grained Event-based Modem Power Model for Enabling In-depth Modem Energy Drain Analysis

Published:19 December 2017Publication History
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Abstract

Cellular modems enable ubiquitous Internet connectivities to modern smartphones, but in doing so they become a major contributor to the smartphone energy drain. Understanding modem energy drain requires a detailed power model. The prior art, an RRC-state based power model, was developed primarily to model the modem energy drain of application data transfer. As such, it serves well its original purpose, but is insufficient to study detailed modem behavior, e.g., activities in the control plane.

In this paper, we propose a new methodology of modeling modem power draw behavior at the event-granularity, and develop to our knowledge the first fine-grained modem power model that captures the power draw of all LTE modem radio-on events in different RRC modes. Second, we quantitatively demonstrate the advantages of the new model over the state-based power model under a wide variety of context via controlled experiments. Finally, using our fine-grained modem power model, we perform the first detailed modem energy drain in-the-wild study involving 12 Nexus 6 phones under normal usage by 12 volunteers spanning a total of 348 days. Our study provides the first quantitative analysis of energy drain due to modem control activities in the wild and exposes their correlation with context such as location and user mobility.

References

  1. {n. d.}. Monsoon Power Monitor. (. {n. d.}). \shownotewww.msoon.com/LabEquipment/PowerMonitor/.Google ScholarGoogle Scholar
  2. 3GPP. 2011. Medium Access Control (MAC) protocol specification. 3GPP.Google ScholarGoogle Scholar
  3. 3GPP. 2011. User Equipment (UE) procedures in idle mode. 3GPP.Google ScholarGoogle Scholar
  4. 3GPP. 2012. Radio Resource Control (RRC);. 3GPP.Google ScholarGoogle Scholar
  5. Pilar Andres-Maldonado, Pablo Ameigeiras, Jonathan Prados-Garzon, Juan J. Ramos-Munoz, and Juan M. Lopez-Soler. 2017. Optimized LTE Data Transmission Procedures for IoT: Device Side Energy Consumption Analysis. Proc. of International workshop on application of green techniques to emerging communication and computing paradigms (GCC).Google ScholarGoogle Scholar
  6. Niranjan Balasubramanian, Aruna Balasubramanian, and Arun Venkataramani. 2009. Energy consumption in mobile phones: a measurement study and implications for network applications. Proc of IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Brooks, V. Tiwari, and M. Martonosi. 2000. Wattch: A framework for architectural-level power analysis and energy estimation. Proc. of ISCA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Catovic, M. Narang, and A. Taha. 2007. Impact of SIB Scheduling on the Standby Battery Life of Mobile Devices in UMTS. 2007 16th IST Mobile and Wireless Communications Summit. 1--5.Google ScholarGoogle Scholar
  9. Xiaomeng Chen, Ning Ding, Abiliash Jindal, Y. Charlie Hu, Maruti Gupta, and Rath Vannithamby. 2015. Smartphone Energy Drain in the Wild: Analysis and Implications. SIGMETRICS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ning Ding, Daniel Wagner, Xiaomeng Chen, Abhinav Pathak, Y. Charlie Hu, and Andrew Rice. 2013. Characterizing and Modeling the Impact of Wireless Signal Strength on Smartphone Battery Drain. SIGMETRICS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Junxian Huang, Feng Qian, Alexandre Gerber, Z. Morley Mao, Subhabrata Sen, and Oliver Spatscheck. 2012. A Close Examination of Performance and Power Characteristics of 4G LTE Networks. Proc. of Mobisys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ali T Koc, Satish C Jha, Rath Vannithamby, and Murat Torlak. 2014. Device power saving and latency optimization in LTE-A networks through DRX configuration. IEEE Transactions on wireless communications, Vol. 13, 5 (2014), 2614--2625.Google ScholarGoogle ScholarCross RefCross Ref
  13. Mads Lauridsen, Laurent Noël, Troels Bundgaard Sørensen, and Preben Mogensen. 2014. An empirical LTE smartphone power model with a view to energy efficiency evolution. Intel Technology Journal Vol. 18, 1 (2014), 172--193.Google ScholarGoogle Scholar
  14. Yuanjie Li, Chunyi Peng, Zengwen Yuan, Jiayao Li, Haotian Deng, and Tao Wang. 2016. Mobileinsight: extracting and analyzing cellular network information on smartphones.. MobiCom. 202--215. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Abhinav Pathak, Y. Charlie Hu, and Ming Zhang. 2012. Where is the energy spent inside my app? Fine Grained Energy Accounting on Smartphones with Eprof. In Proc. of EuroSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Abhinav Pathak, Y. Charlie Hu, Ming Zhang, Paramvir Bahl, and Yi-Min Wang. 2011. Fine-grained Power Modeling for Smartphones Using System-Call Tracing. Proc. of EuroSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Feng Qian, Zhaoguang Wang, Alex Gerber, Z. Morley Mao, Subhabrata Sen, and Oliver Spatscheck. 2010. Characterizing Radio Resource Allocation for 3G Networks. Proc. of IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Sanae Rosen, Ashkan Nikravesh, Yihua Guo, Z. Morley Mao, Feng Qian, and Shubho Sen. 2015. Revisiting Network Energy Efficiency of Mobile Apps: Performance in the Wild. IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Joel Sommers and Paul Barford. 2012. Cell vs. WiFi: On the Performance of Metro Area Mobile Connections. IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Dario Vinella and Michele Polignano. 2009. Discontinuous reception and transmission (DRX/DTX) strategies in long term evolution (LTE) for Voice-Over-IP (VOIP) traffic under both full-dynamic and semi-persistent packet scheduling policies. Project Group Vol. 996 (2009).Google ScholarGoogle Scholar
  21. Qiang Xu, Jeffrey Erman, Alexandre Gerber, Zhuoqing Mao, Jeffrey Pang, and Shobha Venkataraman. 2011. Identifying diverse usage behaviors of smartphone apps. Proc. of IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. W. Ye, N. Vijaykrishnan, M. Kandemir, and M. J. Irwin. 2000. The Design and Use of SimplePower: A Cycle-Accrate Energy Estimation. Proc. of DAC. Google ScholarGoogle ScholarDigital LibraryDigital Library

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