DOI QR코드

DOI QR Code

Evaluating Power Consumption and Real-time Performance of Android CPU Governors

안드로이드 CPU 거버너의 전력 소비 및 실시간 성능 평가

  • Tak, Sungwoo (School of Electrical and Computer Engineering, Pusan National University)
  • Received : 2016.07.18
  • Accepted : 2016.08.04
  • Published : 2016.12.31

Abstract

Android CPU governors exploit the DVFS (Dynamic Voltage Frequency Scaling) technique. The DVFS is a power management technique where the CPU operating frequency is decreased to allow a corresponding reduction in the CPU supply voltage. The power consumed by a CPU is approximately proportional to the square of the CPU supply voltage. Therefore, lower CPU operating frequency allows the CPU supply voltage to be lowered. This helps to reduce the CPU power consumption. However, lower CPU operating frequency increases a task's execution time. Such an increase in the task's execution time makes the task's response time longer and makes the task's deadline miss occur. This finally leads to degrading the quality of service provided by the task. In this paper, we evaluated the performance of Android CPU governors in terms of the power consumption, tasks's response time and deadline miss ratio.

안드로이드 CPU 거버너는 CPU 주파수를 낮추어 CPU 공급 전압을 감소시키는 DVFS (Dynamic Voltage Frequency Scaling) 기반 전력 관리 기법을 사용한다. 그러나 CPU 주파수의 감소는 태스크의 실행 속도 지연을 유발한다. 이로 인해 태스크의 응답 시간 및 마감 시한 초과율이 증가하여 태스크가 제공하는 서비스의 품질 하락이 발생한다. 이에 본 논문에서는 다양한 안드로이드 CPU 거버너들을 전력 소비와 태스크의 응답성 및 마감 시한 측면에서 분석하였다.

Keywords

References

  1. M. Kim, Y. G. Kim, and S. W. Chung, "Measuring variance between smartphone energy consumption and battery life," IEEE Computer, vol. 47, no. 7, pp. 59-65, July 2014.
  2. Y. Zhu, M. Halpern, and V. J. Reddi, "The role of the CPU in energy-efficient mobile web browsing," IEEE Micro, vol. 35, no. 1, pp. 26-33, Jan. 2015. https://doi.org/10.1109/MM.2015.8
  3. V. Seeker, P. Petoumenous, H. Leather, and B. Franke, "Measuring QoE of Load balanced workloads and characterizing frequency governors on mobile devices," in Proceedings of the IEEE International Symposium on Workload Characterization, Raleigh: NC, pp. 61-70, 2014.
  4. V. Pallipadi and A. Starikovskiy, "The ondemand governor," in Proceedings of Linux Symposium, Ottawa: Canada, pp. 223-238, 2006.
  5. W-Y. Liang and P-T Lai, "Design and implementation of a critical speed-based DVFS mechanism for the android operating system," in Proceedings of the 5th International Conference on Embedded and Multimedia Computing, Cebu: Philippine, pp. 1-6, 2010.
  6. A. Shye, B. Scholbrock, and G. Memik, "Into the wild: Studying real user activity patterns to guide power optimizations for mobile architectures," in Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, New York: NY, pp. 168-178, 2009.
  7. J. Yoo, S. Huh, and S. Hong, "Limitations on DVFS-based power saving mechanisms of android smartphones," Communications of the Korea Information Science Society, vol. 30, no. 7, pp. 9-16, July 2012.
  8. T. Burd, and R. Brodsersen, "Energy efficient CMOS microprocessor design," in Proceedings of the 28th Annual Hawaii International Conference on System Sciences. Volume 1: Architecture, Hawaii: USA, pp. 288-297, 1995.
  9. V. Spiliopoulos, A. Bagdia, A. Hansson, P. Aldworth, and S. Kaxiras, "Introducing DVFS-management in a full-system simulator," in Proceedings of the 21st International Symposium on Analysis and Simulation of Computer and Telecommunication Systems, San Francisco: CA, pp. 535-545, 2013
  10. P. Pillai and K. G. Shin, "Real-time dynamic voltage scaling for low-power embedded operating systems," in Proceedings of ACM symposium on Operating Systems Principles, New York: NY, pp. 89-102, 2001.
  11. M. J. Johnson, and K. A. Hawick, "Optimizing energy management of mobile computing devices," in Proceedings of International Conference on Computer Design, Las Vegas: USA, pp. 1-7, 2012.
  12. S. Tak, T. Kim, and E. K. Park, "Integrating real-time inter-task communication channels into hardware-software codesign," Microprocessors and Microsystems, vol. 34, no. 6, pp. 182-199, June 2010. https://doi.org/10.1016/j.micpro.2010.04.002

Cited by

  1. 모바일 스마트 장치 배터리의 남은 시간 예측에 적용 가능한 통계 기법들의 평가 vol.22, pp.2, 2018, https://doi.org/10.6109/jkiice.2018.22.2.284