Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds
YANG Han, CHEN Xu, ZHOU Zhi
(Sun Yat-Sen University, Guangzhou 510275, China)
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.
edge clouds; super resolution sensing; distributed resource monitoring