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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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2020/8867157 |
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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 |
work_keys_str_mv |
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