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Open Access

WAEAS: An Optimization Scheme of EAS Scheduler for Wearable Applications

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
Beijing Information Technology Institute, Beijing 100192, China.
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Abstract

The rapid development of wearable computing technologies has led to an increased involvement of wearable devices in the daily lives of people. The main power sources of wearable devices are batteries; so, researchers must ensure high performance while reducing power consumption and improving the battery life of wearable devices. The purpose of this study is to analyze the new features of an Energy-Aware Scheduler (EAS) in the Android 7.1.2 operating system and the scarcity of EAS schedulers in wearable application scenarios. Also, the paper proposed an optimization scheme of EAS scheduler for wearable applications (Wearable-Application-optimized Energy-Aware Scheduler (WAEAS)). This scheme improves the accuracy of task workload prediction, the energy efficiency of central processing unit core selection, and the load balancing. The experimental results presented in this paper have verified the effectiveness of a WAEAS scheduler.

References

[1]
Y. Liu, D. Liu, and G. Yue, BGMM: A body Gauss-Marko-based mobility model for body area networks, Tsinghua Science And Technology, vol. 23, no. 3, pp. 277-287, 2018.
[2]
G. Li, S. Peng, C. Wang, J. Niu, and Y. Yuan, An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks, Tsinghua Science And Technology, vol. 24, no. 1, pp. 86-96, 2019.
[3]
N. K. Govindaraju, J. Gray, R. Kumar, and D. Manocha, GPUTeraSort: High performance graphics co-processor sorting for large database management, in Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, 2006, pp. 325-336.
[4]
B. Priyantha, D. Lymberopoulos, and J. Liu, Littlerock: Enabling energy-efficient continuous sensing on mobile phones, IEEE Pervasive Computing, vol. 10, no. 2, pp. 12-15, 2011.
[5]
N. D. Lane, P. Georgiev, C. Mascolo, and Y. Gao, Zoe: A cloud-less dialog-enabled continuous sensing wearable exploiting heterogeneous computation, in Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, New York, NY, USA, 2015, pp. 273-286.
[6]
S. Bhattacharya and N. D. Lane, From smart to deep: Robust activity recognition on smart watches using deep learning, presented at 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia, 2016.
[7]
R. Buchty, V. Heuveline, W. Karl, and J. Weiss, A survey on hardware-aware and heterogeneous computing on multicore processors and accelerators, Concurrency and Computation: Practice & Experience, vol. 24, no. 7, pp. 663-675, 2012.
[8]
F. Balarin, L. Lavagno, P. Murthy, and A. Sangiovanni-vincentelli, Scheduling for embedded real-time systems, IEEE Design & Test, vol. 15, no. 1, pp. 71-82, 1998.
[9]
P. Wu and M. Ryu, Best speed fit EDF scheduling for performance asymmetric multiprocessors, Mathematical Problems in Engineering, vol. 2017, pp. 1-7, 2017.
[10]
G. Lipari, Earliest deadline first, http://retis.sssup.it/∼lipari/courses/rtos/lucidi/edf.pdf, 2005.
[11]
H. Khan, Q. Bashir, and M. U. Hashmi, Scheduling-based energy optimization technique in multiprocessor embedded systems, presented at 2018 International Conference on Engineering and Emerging Technologies (ICEET), Lahore, Pakistan, 2018.
[12]
J. Cheng, Research of energy efficient strategy on big.LITTLE architecture for wearable devices, master degree dissertation, Harbin Institute of Technology, Harbin, China, 2017.
[13]
Z. Huang, Cooperative scheduling method with hardware and software for heterogeneous multi-core in embedded system, master degree dissertation, Zhejiang University, Hangzhou, China, 2007.
[14]
X. Gao, Fair scheduling on dynamic heterogeneous chip multiprocessors, master degree dissertation, University of Science and Technology of China, Hefei, China, 2015.
[15]
X. Cong, Research on the embedded low power consumption technology customized for wearable applications, master degree dissertation, Harbin Institute of Technology, Harbin, China, 2018.
[17]
E. S. Gardner, Exponential smoothing: The state of the art, Journal of Forecasting, vol. 4, no. 1, pp. 1-28, 1985.
[18]
P. Turner, CFS per-entity load tracking, https://lwn.net/Articles/504013/, 2012.
[19]
A. Melo, The new Linux ‘perf’ tools, http://www.linux-kongress.org/2010/slides/lk2010-perf-acme.pdf, 2010.
[21]
S. Iqbal, Y. Liang, and H. Grahn, Parmibench, an open-source benchmark for embedded multiprocessor systems, IEEE Computer Architecture Letters, vol. 9, no. 2, pp. 45-48, 2010.
[22]
M. R. Guthaus, J. S. Ringenberg, D. Ernst, T. M. Austin, T. Mudge, and R. B. Brown, MiBench: A free, commercially representative embedded benchmark suite, in Proceedings of the 4th Annual IEEE International Workshop on Workload Characterization, Austin, TX, USA, 2001, pp. 3-14.
Tsinghua Science and Technology
Pages 72-84
Cite this article:
Zhang Z, Cong X, Feng W, et al. WAEAS: An Optimization Scheme of EAS Scheduler for Wearable Applications. Tsinghua Science and Technology, 2021, 26(1): 72-84. https://doi.org/10.26599/TST.2019.9010040

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Received: 24 May 2019
Accepted: 27 August 2019
Published: 19 June 2020
© The author(s) 2021.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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