At present, multi-access edge computing is applied to “cloud-edge-terminal” collaborative computing, in which the task of computing offloading is used to unload the computing tasks of “terminal” to “edge”, i. e. edge servers, but the placement of edge servers is often not considered, resulting in low efficiency of computing offloading. Therefore, this paper proposes a multi-edge collaborative task unloading strategy for the effective placement of edge servers in 5G metropolitan area networks (MAN). First, the strategy weights the average user delay, the average edge server load and the average edge server resource utilization, and improves the firefly algorithm (FA) to optimize the number and effective location of edge servers. Secondly, a multi-edge collaborative task unloading architecture is presented. The computing tasks in this architecture can be executed locally, on a local server, on a remote server or in the “cloud”. The delay and energy consumption of the four unloading modes are modeled respectively. Thirdly, a new variable, i. e. the maximum cooperation cost that the “terminal” can bear, is introduced to attract more remote edge servers to cooperate to complete the calculation of the “terminal” task. Furthermore, the immune particle swarm optimization (IPSO) algorithm was designed to solve the problems of the traditional PSO algorithm. The simulation results show that the improved firefly algorithm can find the optimal coordinates of the edge server, and then carry out the task unloading of the multi-edge server. Compared with the local offload strategy, immune algorithm (IA) and PSO, the total cost of the proposed IPSO algorithm is reduced by 66.7%, 54% and 45.5%, respectively. Therefore, the algorithm in this paper can improve the execution efficiency of computing tasks and effectively reduce the total cost of the whole system.
| Published in | International Journal of Wireless Communications and Mobile Computing (Volume 10, Issue 2) |
| DOI | 10.11648/j.wcmc.20221002.11 |
| Page(s) | 7-17 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Multi-edge Collaboration, Edge Server Placement, Task Offloading, Firefly Algorithm, IPSO Algorithm
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APA Style
Haojie Peng, Yongjie Xu, Hui Li. (2023). A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks. International Journal of Wireless Communications and Mobile Computing, 10(2), 7-17. https://doi.org/10.11648/j.wcmc.20221002.11
ACS Style
Haojie Peng; Yongjie Xu; Hui Li. A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks. Int. J. Wirel. Commun. Mobile Comput. 2023, 10(2), 7-17. doi: 10.11648/j.wcmc.20221002.11
AMA Style
Haojie Peng, Yongjie Xu, Hui Li. A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks. Int J Wirel Commun Mobile Comput. 2023;10(2):7-17. doi: 10.11648/j.wcmc.20221002.11
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Download@article{10.11648/j.wcmc.20221002.11,
author = {Haojie Peng and Yongjie Xu and Hui Li},
title = {A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks},
journal = {International Journal of Wireless Communications and Mobile Computing},
volume = {10},
number = {2},
pages = {7-17},
doi = {10.11648/j.wcmc.20221002.11},
url = {https://doi.org/10.11648/j.wcmc.20221002.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20221002.11},
abstract = {At present, multi-access edge computing is applied to “cloud-edge-terminal” collaborative computing, in which the task of computing offloading is used to unload the computing tasks of “terminal” to “edge”, i. e. edge servers, but the placement of edge servers is often not considered, resulting in low efficiency of computing offloading. Therefore, this paper proposes a multi-edge collaborative task unloading strategy for the effective placement of edge servers in 5G metropolitan area networks (MAN). First, the strategy weights the average user delay, the average edge server load and the average edge server resource utilization, and improves the firefly algorithm (FA) to optimize the number and effective location of edge servers. Secondly, a multi-edge collaborative task unloading architecture is presented. The computing tasks in this architecture can be executed locally, on a local server, on a remote server or in the “cloud”. The delay and energy consumption of the four unloading modes are modeled respectively. Thirdly, a new variable, i. e. the maximum cooperation cost that the “terminal” can bear, is introduced to attract more remote edge servers to cooperate to complete the calculation of the “terminal” task. Furthermore, the immune particle swarm optimization (IPSO) algorithm was designed to solve the problems of the traditional PSO algorithm. The simulation results show that the improved firefly algorithm can find the optimal coordinates of the edge server, and then carry out the task unloading of the multi-edge server. Compared with the local offload strategy, immune algorithm (IA) and PSO, the total cost of the proposed IPSO algorithm is reduced by 66.7%, 54% and 45.5%, respectively. Therefore, the algorithm in this paper can improve the execution efficiency of computing tasks and effectively reduce the total cost of the whole system.},
year = {2023}
}
TY - JOUR T1 - A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks AU - Haojie Peng AU - Yongjie Xu AU - Hui Li Y1 - 2023/01/30 PY - 2023 N1 - https://doi.org/10.11648/j.wcmc.20221002.11 DO - 10.11648/j.wcmc.20221002.11 T2 - International Journal of Wireless Communications and Mobile Computing JF - International Journal of Wireless Communications and Mobile Computing JO - International Journal of Wireless Communications and Mobile Computing SP - 7 EP - 17 PB - Science Publishing Group SN - 2330-1015 UR - https://doi.org/10.11648/j.wcmc.20221002.11 AB - At present, multi-access edge computing is applied to “cloud-edge-terminal” collaborative computing, in which the task of computing offloading is used to unload the computing tasks of “terminal” to “edge”, i. e. edge servers, but the placement of edge servers is often not considered, resulting in low efficiency of computing offloading. Therefore, this paper proposes a multi-edge collaborative task unloading strategy for the effective placement of edge servers in 5G metropolitan area networks (MAN). First, the strategy weights the average user delay, the average edge server load and the average edge server resource utilization, and improves the firefly algorithm (FA) to optimize the number and effective location of edge servers. Secondly, a multi-edge collaborative task unloading architecture is presented. The computing tasks in this architecture can be executed locally, on a local server, on a remote server or in the “cloud”. The delay and energy consumption of the four unloading modes are modeled respectively. Thirdly, a new variable, i. e. the maximum cooperation cost that the “terminal” can bear, is introduced to attract more remote edge servers to cooperate to complete the calculation of the “terminal” task. Furthermore, the immune particle swarm optimization (IPSO) algorithm was designed to solve the problems of the traditional PSO algorithm. The simulation results show that the improved firefly algorithm can find the optimal coordinates of the edge server, and then carry out the task unloading of the multi-edge server. Compared with the local offload strategy, immune algorithm (IA) and PSO, the total cost of the proposed IPSO algorithm is reduced by 66.7%, 54% and 45.5%, respectively. Therefore, the algorithm in this paper can improve the execution efficiency of computing tasks and effectively reduce the total cost of the whole system. VL - 10 IS - 2 ER -
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