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Open Access

LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems

Graduate School, Angeles University Foundation, Angeles City 2009, Philippines, and also with Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China
Graduate School, Angeles University Foundation, Angeles City 2009, Philippines
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Abstract

Edge computing, which migrates compute-intensive tasks to run on the storage resources of edge devices, efficiently reduces data transmission loss and protects data privacy. However, due to limited computing resources and storage capacity, edge devices fail to support real-time streaming data query and processing. To address this challenge, first, we propose a Long Short-Term Memory (LSTM) network-based adaptive approach in the intelligent end-edge-cloud system. Specifically, we maximize the Quality of Experience (QoE) of users by automatically adapting their resource requirements to the storage capacity of edge devices through an event mechanism. Second, to reduce the uncertainty and non-complete adaption of the edge device towards the user’s requirements, we use the LSTM network to analyze the storage capacity of the edge device in real time. Finally, the storage features of the edge devices are aggregated to the cloud to re-evaluate the comprehensive capability of the edge devices and ensure the fast response of the user devices during the dynamic adaptation matching process. A series of experimental results show that the proposed approach has superior performance compared with traditional centralized and matrix decomposition based approaches.

Keywords

quality of experiencedata queryend-edge-cloudLong Short-Term Memory (LSTM) networks

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Tsinghua Science and Technology
Volume 29 Issue 4,
August 2024
Pages 1219-1231
DOI: 10.26599/TST.2023.9010086
Cite this article:
Yang X, Esquivel JA. LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems. Tsinghua Science and Technology, 2024, 29(4): 1219-1231. https://doi.org/10.26599/TST.2023.9010086

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Received: 22 June 2023
Revised: 26 July 2023
Accepted: 10 August 2023
Published: 09 February 2024
© The Author(s) 2024.

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