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.
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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.Google Scholar
- 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. Google Scholar
Digital Library
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- Hardkernel. 2019. ODROID-XU. Retrieved from https://www.hardkernel.com/.Google Scholar
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- Tae-Rok Hwang. 2013. Battery Log, Version 2.0.3. Retrieved from https://play.google.com.Google Scholar
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- Naoki Matsumura, Nobuhiro Otani, and Kiyohiro Hamaji. 2009. Intelligent battery charging rate management. U.S. Patent App. 12/059,967.Google Scholar
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- Myfixguide. 2020. Best Smartphone Processors Ranking. Retrieved from https://www.myfixguide.com/best-smartphone-processors-ranking/.Google Scholar
- Gang Ning and Branko N. Popov. 2004. Cycle life modeling of lithium-ion batteries. J. Electrochem. Soc. 151, 10 (2004), A1584.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- Tina R. Patil. 2013. Performance analysis of Naive Bayes and J48 classification algorithm for data classification. J. Comput. Sci. Appl. 6, 2 (2013).Google Scholar
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
- 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).Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- XDA. 2015. XDA-developersforums. Retrieved from https://forum.xda-developers.com/general/general/ref-to-date-guide-cpu-governors-o-t3048957.Google Scholar
- 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. Google Scholar
Digital Library
- 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.Google Scholar
Cross Ref
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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. Google Scholar
Digital Library
- 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.Google Scholar
Cross Ref
- 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.Google Scholar
Cross Ref
Index Terms
UBAR: User- and Battery-aware Resource Management for Smartphones
Recommendations
Providing an User Centric Always Best Connection
INTERNET '10: Proceedings of the 2010 2nd International Conference on Evolving InternetAlthough Quality of Experience (QoE) is perceived as a subjective measure of a user’s experience, it is the only measure that actually counts to a user of a service. It is essential to identify, quantify and ultimately improve the perception of QoE for ...
A novel user satisfaction prediction model for future network provisioning
The notion of user perception has grown in terms of its importance and complexity. This paper presents results of an experimental study focused on predictive modeling of the relations between the user perception, user satisfaction and objective ...
Edge User Allocation with Dynamic Quality of Service
Service-Oriented ComputingAbstractIn edge computing, edge servers are placed in close proximity to end-users. App vendors can deploy their services on edge servers to reduce network latency experienced by their app users. The edge user allocation (EUA) problem challenges service ...






Comments