Paper
22 May 2024 Edge cloud cooperation environment driven multi-level edge computing method for delay optimization
Honghan Jin, Shiwen Ma, Yi Ding, Xin Deng, Zhenxian Yao, Jinyang Zhao
Author Affiliations +
Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317621 (2024) https://doi.org/10.1117/12.3029024
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China
Abstract
With the continuous and rapid development of the technology of the empowered Internet of Things (IoT), multi-service scenarios continue to appear, which poses a new challenge to the delay of large data services in IoT. Aiming at the cloud and edge cooperation for the IoT architecture, this work proposes a cloud and edge collaboration mechanism for the network delay optimization based on software-defined network, which tries to minimize the average delay by introducing a continuously variable cloud and edge task segmentation coefficient. Besides, we transform the delay optimization problem into a convex optimization one, based on which an optimized task segmentation strategy is obtained. The experimental results indicate that the service delay and network congestion can be reduced greatly by reasonable loading of edge node tasks to the cloud center.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Honghan Jin, Shiwen Ma, Yi Ding, Xin Deng, Zhenxian Yao, and Jinyang Zhao "Edge cloud cooperation environment driven multi-level edge computing method for delay optimization", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317621 (22 May 2024); https://doi.org/10.1117/12.3029024
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KEYWORDS
Clouds

Internet of things

Cloud computing

Computing systems

Distributed computing

Image processing

Performance modeling

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