Joint partial computation offloading and resource allocation in MEC-enable networks

doi:10.19682/j.cnki.1005-8885.2023.2008

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (1): 80-86.doi: 10.19682/j.cnki.1005-8885.2023.2008

Joint partial computation offloading and resource allocation in MEC-enable networks

Wu Hongxin, Lin Zhijian, Chen Pingping, Chen Feng

College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

收稿日期:2021-08-28 修回日期:2022-06-28 接受日期:2023-02-13 出版日期:2023-02-28 发布日期:2023-02-28
通讯作者: Lin Zhijian, E-mail: zlin@fzu.edu.cn E-mail:zlin@fzu.edu.cn
基金资助:
This work was supported by 2020 Science and Technology Innovation Team from Universities of Fujian Province (500190).

Joint partial computation offloading and resource allocation in MEC-enable networks

Wu Hongxin, Lin Zhijian, Chen Pingping, Chen Feng

College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

Received:2021-08-28 Revised:2022-06-28 Accepted:2023-02-13 Online:2023-02-28 Published:2023-02-28
Contact: Lin Zhijian, E-mail: zlin@fzu.edu.cn E-mail:zlin@fzu.edu.cn
Supported by:
This work was supported by 2020 Science and Technology Innovation Team from Universities of Fujian Province (500190).

摘要/Abstract

摘要： The sudden surge of various applications poses great challenges to the computation capability of mobile devices. To address this issue, computation offloading to multi-access edge computing (MEC) was proposed as a promising paradigm. This paper studies partial computation offloading scenario by considering time delay and energy consumption, where the task can be splitted into several blocks and computed both in local devices and MEC, respectively. Since the formulated problem is a nonconvex problem, this paper proposes an ant colony-based algorithm to achieve the suboptimal solution. Specifically, the proposed method first establish a multi-user one-MEC scenario, in which user devices are able to offload some part of the task to MEC server. Then, it develops an ant colony-based algorithm to decide the offloading parts and allocation strategy of MEC resources to minimize system cost. Finally, simulation results show the effectiveness of the proposed algorithm in terms of system cost and demonstrate that it outperforms other existing methods.

关键词: mobile edge computing, resource allocation, partial computation offloading, ant colony-based algorithm

Abstract: The sudden surge of various applications poses great challenges to the computation capability of mobile devices. To address this issue, computation offloading to multi-access edge computing (MEC) was proposed as a promising paradigm. This paper studies partial computation offloading scenario by considering time delay and energy consumption, where the task can be splitted into several blocks and computed both in local devices and MEC, respectively. Since the formulated problem is a nonconvex problem, this paper proposes an ant colony-based algorithm to achieve the suboptimal solution. Specifically, the proposed method first establish a multi-user one-MEC scenario, in which user devices are able to offload some part of the task to MEC server. Then, it develops an ant colony-based algorithm to decide the offloading parts and allocation strategy of MEC resources to minimize system cost. Finally, simulation results show the effectiveness of the proposed algorithm in terms of system cost and demonstrate that it outperforms other existing methods.

Key words: mobile edge computing, resource allocation, partial computation offloading, ant colony-based algorithm

中图分类号:

TP391

Wu Hongxin, Lin Zhijian, Chen Pingping, Chen Feng. Joint partial computation offloading and resource allocation in MEC-enable networks[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(1): 80-86.

参考文献

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Joint partial computation offloading and resource allocation in MEC-enable networks

Joint partial computation offloading and resource allocation in MEC-enable networks

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