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Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks
This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6470783/ https://www.ncbi.nlm.nih.gov/pubmed/30909657 http://dx.doi.org/10.3390/s19061446 |
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author | Huang, Liang Feng, Xu Zhang, Luxin Qian, Liping Wu, Yuan |
author_facet | Huang, Liang Feng, Xu Zhang, Luxin Qian, Liping Wu, Yuan |
author_sort | Huang, Liang |
collection | PubMed |
description | This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm. |
format | Online Article Text |
id | pubmed-6470783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64707832019-04-26 Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks Huang, Liang Feng, Xu Zhang, Luxin Qian, Liping Wu, Yuan Sensors (Basel) Article This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm. MDPI 2019-03-24 /pmc/articles/PMC6470783/ /pubmed/30909657 http://dx.doi.org/10.3390/s19061446 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Liang Feng, Xu Zhang, Luxin Qian, Liping Wu, Yuan Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks |
title | Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks |
title_full | Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks |
title_fullStr | Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks |
title_full_unstemmed | Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks |
title_short | Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks |
title_sort | multi-server multi-user multi-task computation offloading for mobile edge computing networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6470783/ https://www.ncbi.nlm.nih.gov/pubmed/30909657 http://dx.doi.org/10.3390/s19061446 |
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