User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing

The foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for large-scale computing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices. To investigate the relationship between the cost of...

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Main Authors: Zhenquan Qin, Xueyan Qiu, Jin Ye, Lei Wang
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2020/8867157
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spelling doaj-c7b305bccf344eff912b5c0512897a3a2020-11-25T03:14:56ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772020-01-01202010.1155/2020/88671578867157User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge ComputingZhenquan Qin0Xueyan Qiu1Jin Ye2Lei Wang3School of Software, Dalian University of Technology, 116620, ChinaSchool of Software, Dalian University of Technology, 116620, ChinaSchool of Software, Dalian University of Technology, 116620, ChinaSchool of Software, Dalian University of Technology, 116620, ChinaThe foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for large-scale computing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices. To investigate the relationship between the cost of computing tasks and the consumption of time and energy, we propose a computation offloading scheme that achieves lower execution costs by cooperatively allocating computing resources by mobile devices and the edge server. For the mixed-integer nonlinear optimization problem of computing resource allocation and offloading strategy, we segment the problem and propose an iterative optimization algorithm to find the approximate optimal solution. The numerical results of the simulation experiment show that the algorithm can obtain a lower total cost than the baseline algorithm in most cases.http://dx.doi.org/10.1155/2020/8867157
collection DOAJ
language English
format Article
sources DOAJ
author Zhenquan Qin
Xueyan Qiu
Jin Ye
Lei Wang
spellingShingle Zhenquan Qin
Xueyan Qiu
Jin Ye
Lei Wang
User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing
Wireless Communications and Mobile Computing
author_facet Zhenquan Qin
Xueyan Qiu
Jin Ye
Lei Wang
author_sort Zhenquan Qin
title User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing
title_short User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing
title_full User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing
title_fullStr User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing
title_full_unstemmed User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing
title_sort user-edge collaborative resource allocation and offloading strategy in edge computing
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2020-01-01
description The foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for large-scale computing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices. To investigate the relationship between the cost of computing tasks and the consumption of time and energy, we propose a computation offloading scheme that achieves lower execution costs by cooperatively allocating computing resources by mobile devices and the edge server. For the mixed-integer nonlinear optimization problem of computing resource allocation and offloading strategy, we segment the problem and propose an iterative optimization algorithm to find the approximate optimal solution. The numerical results of the simulation experiment show that the algorithm can obtain a lower total cost than the baseline algorithm in most cases.
url http://dx.doi.org/10.1155/2020/8867157
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AT jinye useredgecollaborativeresourceallocationandoffloadingstrategyinedgecomputing
AT leiwang useredgecollaborativeresourceallocationandoffloadingstrategyinedgecomputing
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