research-article

UBAR: User- and Battery-aware Resource Management for Smartphones

Authors:

Nima TaheriNejad,

Samarjit Chakraborty,

Amir M. Rahmani,

Pasi LiljebergAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 3

Article No.: 23, Pages 1 - 25

https://doi.org/10.1145/3441644

Published: 27 March 2021 Publication History

Abstract

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

References

[1]

Saeid Bashash, Scott J. Moura, Joel C. Forman, and Hosam K. Fathy. 2011. Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity. J. Power Sources 196, 1 (2011), 541--549.

[2]

Alberto Bocca, Alessandro Sassone, Alberto Macii, Enrico Macii, and Massimo Poncino. 2015. An aging-aware battery charge scheme for mobile devices exploiting plug-in time patterns. In Proceedings of the 33rd IEEE International Conference on Computer Design (ICCD’15). IEEE, 407--410.

Digital Library

[3]

Aaron Carroll, Gernot Heiser, et al. 2010. An analysis of power consumption in a smartphone. In Proceedings of the USENIX Annual Technical Conference, Vol. 14. Boston, MA, 21--21.

Digital Library

[4]

Jian Chen, Lizy Kurian John, and Dimitris Kaseridis. 2011. Modeling program resource demand using inherent program characteristics. ACM SIGMETRICS Perform. Eval. Rev. 39, 1 (2011), 1--12.

Digital Library

[5]

Min Chen and Gabriel A. Rincon-Mora. 2006. Accurate electrical battery model capable of predicting runtime and IV performance. IEEE Trans. Energy Conv. 21, 2 (2006), 504--511.

[6]

Yukai Chen, Alberto Bocca, Alberto Macii, Enrico Macii, and Massimo Poncino. 2016. A li-ion battery charge protocol with optimal aging-quality of service trade-off. In Proceedings of the International Symposium on Low Power Electronics and Design. 40--45.

Digital Library

[7]

Alexei Colin, Arvind Kandhalu, and Ragunathan Rajkumar. 2014. Energy-efficient allocation of real-time applications onto heterogeneous processors. In Proceedings of the IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications. IEEE, 1--10.

[8]

Sidartha Azevedo Lobo De Carvalho, Daniel Carvalho Da Cunha, and Abel Guilhermino Da Silva-Filho. 2017. Autonomous power management for embedded systems using a non-linear power predictor. In Proceedings of the Euromicro Conference on Digital System Design (DSD’17). IEEE, 22--29.

[9]

Shin Donghwa, Kitae Kim, Naehyuck Chang, Woojoo Lee, Yanzhi Wang, Qing Xie, and Massoud Pedram. 2013. Online estimation of the remaining energy capacity in mobile systems considering system-wide power consumption and battery characteristics. In Proceedings of the 18th Asia and South Pacific Design Automation Conference (ASP-DAC’13). IEEE, 59--64.

[10]

Denzil Ferreira, Anind K. Dey, and Vassilis Kostakos. 2011. Understanding human-smartphone concerns: A study of battery life. In Proceedings of the International Conference on Pervasive Computing. Springer, 19--33.

Digital Library

[11]

Eibe Frank, Mark Hall, and Bernhard Pfahringer. 2002. Locally weighted naive bayes. In Proceedings of the 19th conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers, 249--256.

Digital Library

[12]

Benjamin Gaudette, Carole-Jean Wu, and Sarma Vrudhula. 2016. Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee. In Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA’16). IEEE, 52--63.

[13]

Ujjwal Gupta, Manoj Babu, Raid Ayoub, Michael Kishinevsky, Francesco Paterna, and Umit Y. Ogras. 2018. STAFF: Online learning with stabilized adaptive forgetting factor and feature selection algorithm. In Proceedings of the 55th ACM/ESDA/IEEE Design Automation Conference (DAC’18). IEEE, 1--6.

Digital Library

[14]

Ujjwal Gupta, Chetan Arvind Patil, Ganapati Bhat, Prabhat Mishra, and Umit Y. Ogras. 2017. Dypo: Dynamic pareto-optimal configuration selection for heterogeneous mpsocs. ACM Trans. Embed. Comput. Syst. 16, 5s (2017), 1--20.

Digital Library

[15]

Matthew R. Guthaus, Jeffrey S. Ringenberg, Dan Ernst, Todd M. Austin, Trevor Mudge, and Richard B. Brown. 2001. MiBench: A free, commercially representative embedded benchmark suite. In Proceedings of the 4th Annual IEEE International Workshop on Workload Characterization (WWC’01). IEEE, 3--14.

Digital Library

[16]

Hardkernel. 2019. ODROID-XU. Retrieved from https://www.hardkernel.com/.

[17]

Liang He, Eugene Kim, Kang G. Shin, Guozhu Meng, and Tian He. 2017. Battery state-of-health estimation for mobile devices. In Proceedings of the 8th International Conference on Cyber-Physical Systems. 51--60.

Digital Library

[18]

Henry Hoffmann, Jonathan Eastep, Marco D. Santambrogio, Jason E. Miller, and Anant Agarwal. 2010. Application heartbeats: A generic interface for specifying program performance and goals in autonomous computing environments. In Proceedings of the 7th International Conference on Autonomic Computing. 79--88.

Digital Library

[19]

Tae-Rok Hwang. 2013. Battery Log, Version 2.0.3. Retrieved from https://play.google.com.

[20]

Anil Kanduri, Mohammad-Hashem Haghbayan, Amir M. Rahmani, Pasi Liljeberg, Axel Jantsch, Nikil Dutt, and Hannu Tenhunen. 2016. Approximation knob: Power capping meets energy efficiency. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD’16). IEEE, 1--8.

Digital Library

[21]

Anil Kanduri, Antonio Miele, Amir M. Rahmani, Pasi Liljeberg, Cristiana Bolchini, and Nikil Dutt. 2018. Approximation-aware coordinated power/performance management for heterogeneous multi-cores. In Proceedings of the 55th Annual Design Automation Conference. 1--6.

Digital Library

[22]

Wooseok Lee, Reena Panda, Dam Sunwoo, Jose Joao, Andreas Gerstlauer, and Lizy K. John. 2018. BUQS: Battery-and user-aware QoS scaling for interactive mobile devices. In Proceedings of the 23rd Asia and South Pacific Design Automation Conference (ASP-DAC’18). IEEE, 64--69.

Digital Library

[23]

Naoki Matsumura, Nobuhiro Otani, and Kiyohiro Hamaji. 2009. Intelligent battery charging rate management. U.S. Patent App. 12/059,967.

[24]

Alan Millner. 2010. Modeling lithium ion battery degradation in electric vehicles. In Proceedings of the IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply. IEEE, 349--356.

[25]

Nikita Mishra, Connor Imes, John D. Lafferty, and Henry Hoffmann. 2018. CALOREE: Learning control for predictable latency and low energy. ACM SIGPLAN Notices 53, 2 (2018), 184--198.

Digital Library

[26]

Nikita Mishra, Huazhe Zhang, John D. Lafferty, and Henry Hoffmann. 2015. A probabilistic graphical model-based approach for minimizing energy under performance constraints. ACM SIGARCH Comput. Architect. News 43, 1 (2015), 267--281.

Digital Library

[27]

Thannirmalai Somu Muthukaruppan, Mihai Pricopi, Vanchinathan Venkataramani, Tulika Mitra, and Sanjay Vishin. 2013. Hierarchical power management for asymmetric multi-core in dark silicon era. In Proceedings of the 50th ACM/EDAC/IEEE Design Automation Conference (DAC’13). IEEE, 1--9.

Digital Library

[28]

Myfixguide. 2020. Best Smartphone Processors Ranking. Retrieved from https://www.myfixguide.com/best-smartphone-processors-ranking/.

[29]

Gang Ning and Branko N. Popov. 2004. Cycle life modeling of lithium-ion batteries. J. Electrochem. Soc. 151, 10 (2004), A1584.

[30]

Anuj Pathania, Qing Jiao, Alok Prakash, and Tulika Mitra. 2014. Integrated CPU-GPU power management for 3D mobile games. In Proceedings of the 51st ACM/EDAC/IEEE Design Automation Conference (DAC’14). IEEE, 1--6.

Digital Library

[31]

Tina R. Patil. 2013. Performance analysis of Naive Bayes and J48 classification algorithm for data classification. J. Comput. Sci. Appl. 6, 2 (2013).

[32]

Matthew B. Pinson and Martin Z. Bazant. 2012. Theory of SEI formation in rechargeable batteries: Capacity fade, accelerated aging and lifetime prediction. J. Electrochem. Soc. 160, 2 (2012), A243.

[33]

Alma Pröbstl, Bashima Islam, Shahriar Nirjon, Naehyuck Chang, and Samarjit Chakraborty. 2020. Intelligent chargers will make mobile devices live longer. IEEE Design Test 37, 5 (2020), 42--49.

[34]

Alma Pröbstl, Philipp Kindt, Emanuel Regnath, and Samarjit Chakraborty. 2015. Smart2: Smart charging for smart phones. In Proceedings of the IEEE 21st International Conference on Embedded and Real-Time Computing Systems and Applications. IEEE, 41--50.

Digital Library

[35]

Amir-Mohammad Rahmani, Mohammad-Hashem Haghbayan, Anil Kanduri, Awet Yemane Weldezion, Pasi Liljeberg, Juha Plosila, Axel Jantsch, and Hannu Tenhunen. 2015. Dynamic power management for many-core platforms in the dark silicon era: A multi-objective control approach. In Proceedings of the IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED’15). IEEE, 219--224.

[36]

Basireddy Karunakar Reddy, Geoff V. Merrett, Bashir M. Al-Hashimi, and Amit Kumar Singh. 2018. Online concurrent workload classification for multi-core energy management. In Proceedings of the Design, Automation, and Test in Europe Conference and Exhibition (DATE’18). IEEE, 621--624.

[37]

Hergys Rexha, Simon Holmbacka, and Sébastien Lafond. 2017. Core level utilization for achieving energy efficiency in heterogeneous systems. In Proceedings of the 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP’17). IEEE, 401--407.

[38]

Leonardo M. Rodrigues, Carlos Montez, Ricardo Moraes, Paulo Portugal, and Francisco Vasques. 2017. A temperature-dependent battery model for wireless sensor networks. Sensors 17, 2 (2017), 422.

[39]

Elham Shamsa, Anil Kanduri, Amir M. Rahmani, Pasi Liljeberg, Axel Jantsch, and Nikil Dutt. 2018. Goal formulation: Abstracting dynamic objectives for efficient on-chip resource allocation. In Proceedings of the IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC’18).

[40]

Elham Shamsa, Anil Kanduri, Amir M. Rahmani, Pasi Liljeberg, Axel Jantsch, and Nikil Dutt. 2019. Goal-driven autonomy for efficient on-chip resource management: Transforming objectives to goals. In Proceedings of the Design, Automation, and Test in Europe Conference and Exhibition (DATE’19). IEEE, 1397--1402.

[41]

Elham Shamsa, Anil Kanduri, Nima TaheriNejad, Alma Pröbstl, Samarjit Chakraborty, Amir M. Rahmani, and Pasi Liljeberg. 2020. User-centric resource management for embedded multi-core processors. In Proceedings of the 33rd International Conference on VLSI Design and 19th International Conference on Embedded Systems (VLSID’20). IEEE, 43--48.

[42]

Shervin Sharifi, Dilip Krishnaswamy, and Tajana Šimunić Rosing. 2013. PROMETHEUS: A proactive method for thermal management of heterogeneous MPSoCs. IEEE Trans. Comput.-Aided Design Integr. Circ. Syst. 32, 7 (2013), 1110--1123.

Digital Library

[43]

Yanzhi Wang, Xue Lin, Qing Xie, Naehyuck Chang, and Massoud Pedram. 2014. Minimizing state-of-health degradation in hybrid electrical energy storage systems with arbitrary source and load profiles. In Proceedings of the Design, Automation, and Test in Europe Conference and Exhibition (DATE’14). IEEE, 1--4.

Digital Library

[44]

XDA. 2015. XDA-developersforums. Retrieved from https://forum.xda-developers.com/general/general/ref-to-date-guide-cpu-governors-o-t3048957.

[45]

Qing Xie, Jaemin Kim, Yanzhi Wang, Donghwa Shin, Naehyuck Chang, and Massoud Pedram. 2013. Dynamic thermal management in mobile devices considering the thermal coupling between battery and application processor. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD’13). IEEE, 242--247.

Digital Library

[46]

Rui Xiong, Jiayi Cao, Quanqing Yu, Hongwen He, and Fengchun Sun. 2017. Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access 6 (2017), 1832--1843.

[47]

Kaige Yan, Xingyao Zhang, and Xin Fu. 2015. Characterizing, modeling, and improving the QoE of mobile devices with low battery level. In Proceedings of the 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’15). IEEE, 713--724.

Digital Library

[48]

Kaige Yan, Xingyao Zhang, Jingweijia Tan, and Xin Fu. 2016. Redefining QoS and customizing the power management policy to satisfy individual mobile users. In Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’16). IEEE, 1--12.

Digital Library

[49]

Huazhe Zhang and Henry Hoffmann. 2016. Maximizing performance under a power cap: A comparison of hardware, software, and hybrid techniques. ACM SIGPLAN Notices 51, 4 (2016), 545--559.

Digital Library

[50]

Yancheng Zhang and Chao-Yang Wang. 2009. Cycle-life characterization of automotive lithium-ion batteries with LiNiO2 cathode. J. Electrochem. Soc. 156, 7 (2009), A527.

[51]

Yuhao Zhu, Matthew Halpern, and Vijay Janapa Reddi. 2015. Event-based scheduling for energy-efficient qos (eqos) in mobile web applications. In Proceedings of the IEEE 21st International Symposium on High Performance Computer Architecture (HPCA’15). IEEE, 137--149.

Cited By

Flores-Martin DLaso SHerrera J(2024)Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network BehaviorElectronics10.3390/electronics1324489713:24(4897)Online publication date: 12-Dec-2024
https://doi.org/10.3390/electronics13244897
Shakibhamedan SAminifar AVassallo LTaheriNejad N(2024)Harnessing Approximate Computing for Machine Learning2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI61997.2024.00110(585-591)Online publication date: 1-Jul-2024
https://doi.org/10.1109/ISVLSI61997.2024.00110
Lu YHu CGong YCheng D(2024)Improving User Experience via Reinforcement Learning-Based Resource Management on Mobile DevicesAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_31(383-395)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5581-3_31
Show More Cited By

Index Terms

UBAR: User- and Battery-aware Resource Management for Smartphones
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. System on a chip
2. Hardware
  1. Power and energy

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 20, Issue 3

May 2021

217 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3458920

Editor:
Tulika Mitra
National University of Singapore, Singapore

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Copyright © 2021 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 27 March 2021

Accepted: 01 December 2020

Revised: 01 October 2020

Received: 01 April 2020

Published in TECS Volume 20, Issue 3

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Cited By

Flores-Martin DLaso SHerrera J(2024)Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network BehaviorElectronics10.3390/electronics1324489713:24(4897)Online publication date: 12-Dec-2024
https://doi.org/10.3390/electronics13244897
Shakibhamedan SAminifar AVassallo LTaheriNejad N(2024)Harnessing Approximate Computing for Machine Learning2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI61997.2024.00110(585-591)Online publication date: 1-Jul-2024
https://doi.org/10.1109/ISVLSI61997.2024.00110
Lu YHu CGong YCheng D(2024)Improving User Experience via Reinforcement Learning-Based Resource Management on Mobile DevicesAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_31(383-395)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5581-3_31
Raptis TBedogni L(2023)GreenCrowd: Toward a Holistic Algorithmic Crowd Charging FrameworkIEEE Pervasive Computing10.1109/MPRV.2023.330801422:4(58-65)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/MPRV.2023.3308014
Isuwa SDey SOrtega ASingh AAl-Hashimi BMerrett G(2022)QUAREM: Maximising QoE Through Adaptive Resource Management in Mobile MPSoC PlatformsACM Transactions on Embedded Computing Systems10.1145/352611621:4(1-29)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1145/3526116
Ojha TRaptis TConti MPassarella A(2022)Wireless Crowd Charging with Battery Aging Mitigation2022 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP55677.2022.00034(142-149)Online publication date: Jun-2022
https://doi.org/10.1109/SMARTCOMP55677.2022.00034
Baroughi AHuemer SShahhoseini HTaheriNejad N(2022)AxE: An Approximate-Exact Multi-Processor System-on-Chip Platform2022 25th Euromicro Conference on Digital System Design (DSD)10.1109/DSD57027.2022.00018(60-66)Online publication date: Aug-2022
https://doi.org/10.1109/DSD57027.2022.00018
Ray KBanerjee A(2022)Preference-Aware Computation Offloading for IoT in Multi-access Edge Computing Using Probabilistic Model CheckingQuantitative Evaluation of Systems10.1007/978-3-031-16336-4_14(275-297)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-16336-4_14

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