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

作　　者：Xuan Yang James A.Esquivel 

机构地区：[1]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 [2]Graduate School,Angeles University Foundation,Angeles City 2009,Philippines

出　　处：《Tsinghua Science and Technology》2024年第4期1219-1231,共13页清华大学学报自然科学版（英文版）

摘　　要：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.

关 键 词：quality of experience data query end-edge-cloud Long Short-Term Memory(LSTM)networks 

分 类 号：TP308[自动化与计算机技术—计算机系统结构]