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V-edge: Fast Self-constructive Power Modeling of Smartphones Based on Battery Voltage Dynamics

Fengyuan Xu, Yunxin Liu, Qun Li, Yongguang Zhang
2013 Symposium on Networked Systems Design and Implementation  
System power models are important for power management and optimization on smartphones. However, existing approaches for power modeling have several limitations.  ...  Most importantly, it is fast in model building. Our implementation supports both component level power models and per-application energy accounting.  ...  We thank Thomas Moscibroda, Ranveer Chandra, our shepherd Dave Levin, and anonymous reviewers for their valuable comments and insightful feedback.  ... 
dblp:conf/nsdi/XuLLZ13 fatcat:m25vx4mlmzcghdjhk57bv3umm4

AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring

Chanmin Yoon, Dongwon Kim, Wonwoo Jung, Chulkoo Kang, Hojung Cha
2012 USENIX Annual Technical Conference  
The evaluation results indicate that AppScope accurately estimates the energy consumption of Android applications expending approximately 35mW and 2.1% in power consumption and CPU utilization overhead  ...  The energy consumption of a smartphone application is, therefore, often estimated with low accuracy and granularity. In this paper, we propose AppScope, an Android-based energy metering system.  ...  Acknowledgements We would like to thank the anonymous reviewers for their comments.  ... 
dblp:conf/usenix/YoonKJKC12 fatcat:gatm2euhong6jhuf7nmzapk7uu

STOC: Energy Cost Models of Smartphones for Task Offloading to the Cloud

2016 International Journal of Science and Research (IJSR)  
Accurate energy estimation models will enable these devices to make the right decisions as to whether or not to perform task offloading, based on the energy cost of the communication activities.  ...  Improving computing ability of smartphones and also prolonging their battery life can be achieved by offloading the task to the cloud.  ...  helps in reducing power consumption and increasing the processing efficiency of smartphones.  ... 
doi:10.21275/v5i6.nov164814 fatcat:42wcxkqch5feplgmc5cwg3va5q

Towards better CPU power management on multicore smartphones

Yifan Zhang, Xudong Wang, Xuanzhe Liu, Yunxin Liu, Łi Zhuang, Feng Zhao
2013 Proceedings of the Workshop on Power-Aware Computing and Systems - HotPower '13  
Furthermore, we find that the existing CPU power models on smartphones are ill-suited for modern multicore CPUs. We develop a new CPU power model with a high accuracy, 95.6% on average.  ...  Our work helps to better understand the performance of multicore smartphones and paves the way towards better CPU power management on multicore smartphones.  ...  Then we show that the existing CPU power models do not work well on multicore smartphones and propose a new power model for accurate CPU power modeling.  ... 
doi:10.1145/2525526.2525849 dblp:conf/sosp/ZhangWLLZZ13 fatcat:mjlrf3ym4fahzmsmcj6r3gvsde

Update rate tradeoffs for improving online power modeling in smartphones

Frank Maker, Rajaveen Amirtharajah, Venkatesh Akella
2013 International Symposium on Low Power Electronics and Design (ISLPED)  
Offline smartphone power modeling with benchtop equipment is cumbersome for software developers and takes substantial time to perform on multiple devices.  ...  By running on the device itself, online modeling can be performed dynamically and is scalable to many different smartphones.  ...  To estimate battery life BMUs have built-in temperature, voltage, and current ADCs. These devices must be highly energy efficient to avoid diverting significant power from the smartphone itself.  ... 
doi:10.1109/islped.2013.6629276 dblp:conf/islped/MakerAA13 fatcat:t5zyal5425hqrl37hce4jyu3dm

Fine-grained power modeling for smartphones using system call tracing

Abhinav Pathak, Y. Charlie Hu, Ming Zhang, Paramvir Bahl, Yi-Min Wang
2011 Proceedings of the sixth conference on Computer systems - EuroSys '11  
Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications  ...  We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior.  ...  Acknowledgments We thank the program committee and reviewers for their helpful comments, and especially our shepherd, M.  ... 
doi:10.1145/1966445.1966460 dblp:conf/eurosys/PathakHZBW11 fatcat:qx2ks75mc5dkjg6x7gsbo3x55m

Accurate online power estimation and automatic battery behavior based power model generation for smartphones

Lide Zhang, Birjodh Tiwana, Zhiyun Qian, Zhaoguang Wang, Robert P. Dick, Zhuoqing Morley Mao, Lei Yang
2010 Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis - CODES/ISSS '10  
We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation.  ...  Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.  ...  The models produced by these techniques provide accurate, real-time power consumption estimates for powerintensive Android platform smartphone components including CPU, LCD, GPS, and audio, as well as  ... 
doi:10.1145/1878961.1878982 dblp:conf/codes/ZhangTQWDMY10 fatcat:ebyeav6a5vhnho2pe54ndzwgya

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

Pijush Kanti Dutta Pramanik, Nilanjan Sinhababu, Bulbul Mukherjee, Sanjeevikumar Padmanaban, Aranyak Maity, Bijoy Kumar Upadhyaya, Jens Bo Holm-Nielsen, Prasenjit Choudhury
2019 IEEE Access  
The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power  ...  Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone.  ...  [243] which combines both the utilization-based model and the FSM-based model to analyse and estimate the impacts of CPU, GPU, cellular, Wi-Fi and different apps running on the mobile phones on power  ... 
doi:10.1109/access.2019.2958684 fatcat:52m6kmfxcngpdmhkyzmcsw4qk4

Energy modeling of system settings: A crowdsourced approach

Ella Peltonen, Eemil Lagerspetz, Petteri Nurmi, Sasu Tarkoma
2015 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)  
and hardware power measurements.  ...  remains a common source of frustration for many smartphone users.  ...  The authors are grateful to Dr Stephan Sigg, Teemu Pulkkinen, and Samuli Hemminki for comments on earlier versions of the paper.  ... 
doi:10.1109/percom.2015.7146507 dblp:conf/percom/PeltonenLNT15 fatcat:x2poumqggbgppadyyo3s2kfmae

AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning [article]

Young Geun Kim, Carole-Jean Wu
2021 arXiv   pre-print
By considering the unique characteristics of FL edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence time and 4.7 and 5.2 times higher energy efficiency for local clients and  ...  We propose AutoFL by tailor-designing a reinforcement learning algorithm that learns and determines which K participant devices and per-device execution targets for each FL model aggregation round in the  ...  When the CPU is selected as the execution target, E comp is calculated using a utilization-based CPU power model [13, 53, 62, 133] as in (1) , where E i core is the power consumed by the ith core, t  ... 
arXiv:2107.08147v1 fatcat:7bnw4ktnebhnlfs6dnkjcvg4fy

BET Estimation on Power Saving by Intermittent Disabling Network Interface on Android

Tsubasa Murakami, Takeshi Kamiyama, Akira Fukuda, Masato Oguchi, Saneyasu Yamaguchi
2019 Journal of Information Processing  
We evaluate the methods with practical applications and Android devices and show that the method based on the average electric current can estimate BET accurately.  ...  In this paper, we focus on a method of reducing power consumption in the screen-off state by repeating to disable and enable the network interface and discuss estimation of its Break-Even Time (BET) with  ...  These works on modeling and analyzing the power consumptions of smartphones are mainly based on existing works for PCs, such as Refs. [16] , [17] and extended them for smartphones.  ... 
doi:10.2197/ipsjjip.27.671 fatcat:i5hnkzfdinfjfgreepzc7q67ja

Energy and Performance of Smartphone Radio Bundling in Outdoor Environments

Ana Nika, Yibo Zhu, Ning Ding, Abhilash Jindal, Y. Charlie Hu, Xia Zhou, Ben Y. Zhao, Haitao Zheng
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15  
We study the links between traffic partitioning and bundling performance, and use a novel componentized energy model to quantify the energy consumed by CPUs (and radios) during traffic management.  ...  Our results show that MPTCP achieves only a fraction of the total performance gain possible, and that its energy-agnostic design leads to considerable power consumption by the CPU.  ...  We develop accurate power models for the dual-core CPU and WiFi and LTE interfaces for the phones used in our experiments.  ... 
doi:10.1145/2736277.2741635 dblp:conf/www/NikaZDJHZZZ15 fatcat:pom4sgjwibdypjwiu4kuenpkjy

Sensing Human-Screen Interaction for Energy-Efficient Frame Rate Adaptation on Smartphones

Jiadi Yu, Haofu Han, Hongzi Zhu, Yingying Chen, Jie Yang, Yanmin Zhu, Guangtao Xue, Minglu Li
2015 IEEE Transactions on Mobile Computing  
use the highest frame rate which arouses huge computation burden and can contribute nearly 50 percent to the total power consumption of smartphones.  ...  On average, E 3 can save up to 60 percent of the energy consumed by CPU and 35 percent of the overall energy consumption.  ...  Such an approach enables the uniform model to become more and more accurate and fits all the users better.  ... 
doi:10.1109/tmc.2014.2352862 fatcat:barwq2xixjfxrcyciiok4fo3le

Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning [article]

Dong-Hyun Hwang, Suntae Kim, Nicolas Monet, Hideki Koike, Soonmin Bae
2020 arXiv   pre-print
To improve the overall performance of the model, we apply the teacher-student learning method based knowledge distillation to 3D human pose estimation.  ...  Extensive evaluations show the advantages of our lightweight model with the proposed training method over previous 3D pose estimation methods on the Human3.6M dataset and mobile devices.  ...  Our lightweight model trained with efficient training method enables accurate pose estimation with very low computation, which can operate on devices with low processing power.  ... 
arXiv:2001.05097v1 fatcat:yr36am6ayzbqrkh6spslhxiyam

Survey on adaptation techniques of energy consumption within a smartphone

Khalil Ibrahim Hamzaoui, Gilles Grimaud, Mostafa Azizi, Mohammed Berrajaa, Abdelkader Betari
2014 2014 Science and Information Conference  
The energy consumption into a smartphone is defined by the energy cost necessary for the components equipment to achieve their activities.  ...  As results of this work, we conclude that some of the investigated techniques are more accurate than the others for tracking the main sources or equipment responsible of consuming energy.  ...  Eprof is a model based on the use of power; it does not reflect the behavior of asynchronous power found in modern smartphones. D.  ... 
doi:10.1109/sai.2014.6918197 fatcat:c2rvc3xxyzeyrpc5y4nhxvfcoy
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