Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

doi:10.12142/ZTECOM.202103009

ZTE Communications ›› 2021, Vol. 19 ›› Issue (3): 73-80.DOI: 10.12142/ZTECOM.202103009

• Research Paper • Previous Articles Next Articles

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

YANG Han, CHEN Xu(), ZHOU Zhi

Sun Yat-Sen University, Guangzhou 510275, China

Received:2021-04-17 Online:2021-09-25 Published:2021-10-11
About author:YANG Han received the B.Eng. degree from Sun Yat-Sen University, China in 2019. He is currently pursuing his master’s degree at Sun Yat-Sen University. His research interests include edge computing and edge intelligence.|CHEN Xu (chenxu35@mail.sysu.edu.cn) is a full professor at Sun Yat-Sen University, China, and the vice director of National and Local Joint Engineering Laboratory of Digital Home Interactive Applications. He received the Ph.D. degree in information engineering from The Chinese University of Hong Kong, China in 2012, and worked as a post-doctoral research associate at Arizona State University, USA from 2012 to 2014 and a Humboldt Scholar Fellow at Institute of Computer Science of University of Goettingen, Germany from 2014 to 2016. He is currently an area editor of IEEE Open Journal of the Communications Society, an associate editor of the IEEE Transactions Wireless Communications, IEEE Internet of Things Journal and IEEE Journal on Selected Areas in Communications (JSAC) series on network softwarization and enablers.|ZHOU Zhi received the B.S., M.E. and Ph.D. degrees in 2012, 2014 and 2017, respectively, all from the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST), China. He is currently an associate professor in the School of Computer Science and Engineering, Sun Yat-Sen University, China. In 2016, he was a visiting scholar at University of Goettingen, Germany. He was nominated for the 2019 CCF Outstanding Doctoral Dissertation Award, the sole recipient of the 2018 ACM Wuhan & Hubei Computer Society Doctoral Dissertation Award, and a recipient of the Best Paper Award of IEEE UIC 2018. His research interests include edge computing, cloud computing, and distributed systems.
Supported by:
the National Key Research and Development Program of China(2017YFB1001703)

Abstract

Abstract:

With the vigorous development of mobile networks, the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden. Existing resource monitoring systems are widely deployed in cloud data centers, but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices. To address these challenges, we propose a super resolution sensing (SRS) method for distributed resource monitoring, which can be used to recover reliable and accurate high-frequency data from low-frequency sampled resource monitoring data. Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low-frequency monitoring data to high-frequency data, and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.

Key words: edge clouds, super resolution sensing, distributed resource monitoring

YANG Han, CHEN Xu, ZHOU Zhi. Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds[J]. ZTE Communications, 2021, 19(3): 73-80.

Figures/Tables 8

References 28

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f_l	f_h	SRF	Linear Interpolation (MAPE/PSNR/DTW)	Cubic Interpolation (MAPE/PSNR/DTW)	CS (MAPE/PSNR/DTW)	SRS (MAPE/PSNR/DTW)
1/20	1/10	2	4.78%/33.58/334.62	4.66%/33.73/317.26	11.30%/27.04/678.56	4.15%/34.78/296.52
1/50	1/10	5	7.23%/29.89/493.52	7.16%/29.86/472.10	23.03%/20.93/1 499.84	6.32%/30.95/442.34
1/100	1/10	10	8.99%/27.99/623.26	9.10%/27.89/600.04	30.85%/19.06/2 157.87	8.09%/28.72/548.06
1/200	1/10	20	11.93%/26.02/781.07	11.02%/26.52/757.24	36.45%/18.42/2 649.60	9.85%/27.03/696.51
1/400	1/10	40	13.16%/24.30/936.30	12.62%/24.35/918.10	106.59%/15.34/8 549.43	11.09%/26.01/817.40
1/100	1/50	2	5.33%/32.66/78.78	5.33%/32.69/74.23	14.90%/24.85/191.03	4.84%/33.91/70.99
1/100	1/20	5	7.72%/29.34/264.58	7.81%/29.92/252.15	24.42%/20.77/819.10	6.98%/30.29/241.26
1/200	1/20	10	9.78%/27.41/347.59	9.99%/27.19/334.94	31.70%/16.64/1 159.87	8.94%/28.03/318.82
1/400	1/20	20	11.23%/26.38/428.85	11.13%/26.03/419.25	82.49%/16.16/3 458.83	10.26%/26.79/379.41

f_l	f_h	SRF	Linear Interpolation (MAPE/PSNR/DTW)	Cubic Interpolation (MAPE/PSNR/DTW)	CS (MAPE/PSNR/DTW)	SRS (MAPE/PSNR/DTW)
1/20	1/10	2	4.78%/33.58/334.62	4.66%/33.73/317.26	11.30%/27.04/678.56	4.15%/34.78/296.52
1/50	1/10	5	7.23%/29.89/493.52	7.16%/29.86/472.10	23.03%/20.93/1 499.84	6.32%/30.95/442.34
1/100	1/10	10	8.99%/27.99/623.26	9.10%/27.89/600.04	30.85%/19.06/2 157.87	8.09%/28.72/548.06
1/200	1/10	20	11.93%/26.02/781.07	11.02%/26.52/757.24	36.45%/18.42/2 649.60	9.85%/27.03/696.51
1/400	1/10	40	13.16%/24.30/936.30	12.62%/24.35/918.10	106.59%/15.34/8 549.43	11.09%/26.01/817.40
1/100	1/50	2	5.33%/32.66/78.78	5.33%/32.69/74.23	14.90%/24.85/191.03	4.84%/33.91/70.99
1/100	1/20	5	7.72%/29.34/264.58	7.81%/29.92/252.15	24.42%/20.77/819.10	6.98%/30.29/241.26
1/200	1/20	10	9.78%/27.41/347.59	9.99%/27.19/334.94	31.70%/16.64/1 159.87	8.94%/28.03/318.82
1/400	1/20	20	11.23%/26.38/428.85	11.13%/26.03/419.25	82.49%/16.16/3 458.83	10.26%/26.79/379.41

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

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