research-article

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

Authors:

Li Ping QianAuthors Info & Claims

Mobile Networks and Applications, Volume 27, Issue 3

Pages 1123 - 1130

https://doi.org/10.1007/s11036-018-1177-x

Published: 01 June 2022 Publication History

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.

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Cited By

Sharma NGhosh AMisra RDas S(2024)Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2023.326490123:4(2583-2598)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3264901
Nandi PReaj MSarker SRazzaque MMamun-or-Rashid MRoy P(2024)Task offloading to edge cloud balancing utility and cost for energy harvesting Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2023.103766221:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.jnca.2023.103766
Gong BJiang X(2023)Dependent Task-Offloading Strategy Based on Deep Reinforcement Learning in Mobile Edge ComputingWireless Communications & Mobile Computing10.1155/2023/46650672023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/4665067
Show More Cited By

Index Terms

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
1. Mathematics of computing
  1. Mathematical analysis
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Mobile Networks and Applications

Mobile Networks and Applications Volume 27, Issue 3

Jun 2022

488 pages

ISSN:1383-469X

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2018.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2022

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Funding Sources

the National Natural Science Foundation of China
the National Natural Science Foundation of China under Grant
the Zhejiang Provincial Natural Science Foundation of China under Grant
the Zhejiang Provincial Natural Science Foundation of China under Grant

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Cited By

Sharma NGhosh AMisra RDas S(2024)Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2023.326490123:4(2583-2598)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3264901
Nandi PReaj MSarker SRazzaque MMamun-or-Rashid MRoy P(2024)Task offloading to edge cloud balancing utility and cost for energy harvesting Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2023.103766221:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.jnca.2023.103766
Gong BJiang X(2023)Dependent Task-Offloading Strategy Based on Deep Reinforcement Learning in Mobile Edge ComputingWireless Communications & Mobile Computing10.1155/2023/46650672023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/4665067
Li MXia DZhang W(2023)Dynamic Offloading Task for Internet of Things Based on Meta Supervised LearningProceedings of the 2023 2nd International Conference on Networks, Communications and Information Technology10.1145/3605801.3605819(87-92)Online publication date: 16-Jun-2023
https://dl.acm.org/doi/10.1145/3605801.3605819
Hua HLi YWang TDong NLi WCao J(2023)Edge Computing with Artificial Intelligence: A Machine Learning PerspectiveACM Computing Surveys10.1145/355580255:9(1-35)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3555802
Du JWu HXu MBuyya R(2023)Computation Energy Efficiency Maximization for NOMA-Based and Wireless-Powered Mobile Edge Computing With Backscatter CommunicationIEEE Transactions on Mobile Computing10.1109/TMC.2023.332861223:6(6954-6970)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3328612
Chen MGuo ASong C(2023)Multi-agent deep reinforcement learning for collaborative task offloading in mobile edge computing networksDigital Signal Processing10.1016/j.dsp.2023.104127140:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.dsp.2023.104127
Taheri-abed SEftekhari Moghadam ARezvani M(2023)Machine learning-based computation offloading in edge and fog: a systematic reviewCluster Computing10.1007/s10586-023-04100-z26:5(3113-3144)Online publication date: 21-Jul-2023
https://dl.acm.org/doi/10.1007/s10586-023-04100-z
Goudarzi MPalaniswami MBuyya R(2022)Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future DirectionsACM Computing Surveys10.1145/354483655:7(1-41)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3544836
Chandrika PMekala MSrivastava G(2022)Edge resource slicing approaches for latency optimization in AI-edge orchestrationCluster Computing10.1007/s10586-022-03817-726:2(1659-1683)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/s10586-022-03817-7

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