Abstract
Smartphones are now equipped with powerful application processors to meet the requirement of performance demanding apps. This often leads to over-provisioned hardware, which drains the battery quickly. To save energy for battery-powered smartphones, it is necessary to let the processor run at a power-saving state without sacrificing user experience. Android has implemented a set of CPU governors by leveraging dynamic voltage and frequency scaling (DVFS) according to the computational requirements of apps. However, they commonly adopt very conservative policies due to limited information, therefore leaving considerable energy reduction opportunities unexplored; or they are not responsive to user interactions, leading to poor user experiences. In this work, we start from an important observation on the repetitive patterns of smartphone usage for each individual user, and propose UH-DVFS, a usage history-directed DVFS framework leveraging on this observation. UH-DVFS can identify repetitive user transactions, and store their execution history information within a table. When such a user transaction is launched again, the table is consulted for an appropriate CPU frequency adaptation to reduce the energy consumption of the user transaction without sacrificing user experience. We have implemented the proposed framework on Android smartphones, and have tested it with real-world interaction-intensive apps. The results show that our framework can save energy from 10 % to 36 % without affecting the quality of user experiences.
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References
Android monkeyrunner. http://developer.android.com/tools/help/MonkeyRunner.html
Monsoon power monitor. https://www.msoon.com/
Bai, Y.: Memory characterization to analyze and predict multimedia performance and power in an application processor. Marvell White Paper (2011)
Chang, Y.-M., Hsiu, P.-C., Chang, Y.-H., Chang, C.-W.: A resource-driven dvfs scheme for smart handheld devices. ACM Trans. Embed. Comput. Syst. (TECS) 13(3), 53 (2013)
Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 179–194, ACM (2010)
Kang, J.-M., Seo, S.s., Hong, J.W.-K.: Usage pattern analysis of smartphones. In: 2011, 13th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–8, September 2011
Kim, S., Kim, H., Hwang, J., Lee, J., Seo, E.: An event-driven power management scheme for mobile consumer electronics. IEEE Trans. Consum. Electron. 59(1), 259–266 (2013)
Le Sueur, E., Heiser, G.: Dynamic voltage and frequency scaling: the laws of diminishing returns. In: Proceedings of the 2010 International Conference on Power Aware Computing and Systems, pp. 1–8. USENIX Association (2010)
Pallipadi, V., Starikovskiy, A.: The ondemand governor. In: Proceedings of the Linux Symposium, vol. 2, pp. 215–230, sn (2006)
Pathak, A., Hu, Y.C., Zhang, M.: 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, pp. 29–42, ACM (2012)
Pathak, A., Hu, Y.C., Zhang, M., Bahl, P., Wang, Y.-M.: Fine-grained power modeling for smartphones using system call tracing. In: Proceedings of the Sixth Conference on Computer Systems, pp. 153–168, ACM (2011)
Pathania, A., Jiao, Q., Prakash, A., Mitra, T.: Integrated cpu-gpu power management for 3d mobile games. In: 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–6, IEEE (2014)
Shye, A., Scholbrock, B., Memik, G.: 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, pp. 168–178, ACM (2009)
Song, W., Sung, N., Chun, B.-G., Kim, J.: Reducing energy consumption of smartphones using user-perceived response time analysis. In: Proceedings of the 15th Workshop on Mobile Computing Systems and Applications, pp. 20, ACM (2014)
Zhang, L., Bild, D.R., Dick, R.P., Mao, Z.M., Dinda, P.: Panappticon: event-based tracing to measure mobile application and platform performance. In: 2013 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS), pp. 1–10, IEEE (2013)
Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R.P., Mao, Z.M., Yang, L.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 105–114, ACM (2010)
Acknowledgments
This work is supported by the grant of Shenzhen municipalgovernment for basic research on Information Technologies (No. JCYJ20130331144751105).
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Li, X., Wen, W., Wang, X. (2015). Usage History-Directed Power Management for Smartphones. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9528. Springer, Cham. https://doi.org/10.1007/978-3-319-27119-4_20
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DOI: https://doi.org/10.1007/978-3-319-27119-4_20
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