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Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 3/2022

29.11.2018

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

verfasst von: Liang Huang, Xu Feng, Anqi Feng, Yupin Huang, Li Ping Qian

Erschienen in: Mobile Networks and Applications | Ausgabe 3/2022

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Abstract

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) choose to offload their computation tasks to an edge server. To conserve energy and maintain quality of service for WDs, the optimization of joint offloading decision and bandwidth allocation is formulated as a mixed integer programming problem. However, the problem is computationally limited by the curse of dimensionality, which cannot be solved by general optimization tools in an effective and efficient way, especially for large-scale WDs. In this paper, we propose a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions. We adopt a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs. Extensive numerical results show that the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second.
Vorheriger Artikel Online Learning and Optimization for Computation Offloading in D2D Edge Computing and Networks
Nächster Artikel Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks

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1
The source code of scipy is available at https://​www.​scipy.​org.
 
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Metadaten
Titel
Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
verfasst von
Liang Huang
Xu Feng
Anqi Feng
Yupin Huang
Li Ping Qian
Publikationsdatum
29.11.2018
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 3/2022
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-018-1177-x

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