基于智能化用户协作的边缘计算任务卸载与资源分配优化

doi:10.11959/j.issn.2096-3750.2022.00303

物联网学报 ›› 2022, Vol. 6 ›› Issue (4): 41-52.doi: 10.11959/j.issn.2096-3750.2022.00303

基于智能化用户协作的边缘计算任务卸载与资源分配优化

李贤¹, 毕宿志¹^,², 曾泓儒¹, 林彬¹, 林晓辉¹

¹ 深圳大学电子与信息工程学院，广东深圳 518060
² 鹏城实验室宽带通信研究部，广东深圳 518066

修回日期:2022-09-23 出版日期:2022-12-30 发布日期:2022-12-01
作者简介:李贤（1988- ），男，博士，深圳大学电子与信息工程学院副研究员，主要研究方向为移动边缘计算、无线供电通信等系统的性能优化设计
毕宿志（1987- ），男，博士，深圳大学电子与信息工程学院副教授，主要研究方向为无线通信网络资源管理与调度优化
曾泓儒（1996- ），男，深圳大学电子与信息工程学院硕士生，主要研究方向为移动边缘计算系统的优化设计
林彬（1997- ），男，深圳大学电子与信息工程学院硕士生，主要研究方向为移动边缘计算系统的优化设计
林晓辉（1975- ），男，博士，深圳大学电子与信息工程学院教授，主要研究方向为无人机通信网络的优化设计
基金资助:
国家重点研发计划(2019YFB1803305);国家自然科学基金资助项目(61871271);国家自然科学基金资助项目(62271325);鹏城实验室宽带通信研究部重点研究计划;广东省教育厅科技重点专项(2020ZDZX3050);广东省基础与应用基础研究基金资助项目(2022A1515011219);广东省基础与应用基础研究基金资助项目(2022A1515010973);深圳市科创委基础研究项目(20220810142637001);深圳市科创委基础研究项目(JCYJ20210324093011030);深圳市科创委基础研究项目(JCYJ20190808120415286);智慧城市物联网国家重点实验室（澳门大学）开放课题(SKL-IoTSC(UM)-2021-2023/ORPF/A03/2022)

Collaborative task offloading and resource allocation optimization for intelligent edge devices

Xian LI¹, Suzhi BI¹^,², Hongru ZENG¹, Bin LIN¹, Xiaohui LIN¹

¹ College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
² Pengcheng Laboratory, Broadband Communication Research Department, Shenzhen 518066, China

Revised:2022-09-23 Online:2022-12-30 Published:2022-12-01
Supported by:
The National Key Research and Development Program(2019YFB1803305);The National Natural Science Foundation of China(61871271);The National Natural Science Foundation of China(62271325);The Major Key Project of PCL Department of Broadband Communication;The Key Project of Department of Education of Guangdong Province(2020ZDZX3050);Guangdong Basic and Applied Basic Research Foundation(2022A1515011219);Guangdong Basic and Applied Basic Research Foundation(2022A1515010973);The Shenzhen Science and Technology Program(20220810142637001);The Shenzhen Science and Technology Program(JCYJ20210324093011030);The Shenzhen Science and Technology Program(JCYJ20190808120415286);The Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City (University of Macau)(SKL-IoTSC(UM)-2021-2023/ORPF/A03/2022)

摘要/Abstract

摘要：

为了解决移动边缘计算网络中计算资源日益紧缺的问题，设计了一种基于用户协作的边缘计算资源分配机制，充分利用用户之间的空闲计算资源，有效提升系统整体的数据处理性能。以最大化用户的效用函数为目标，将目标优化问题建模为一个关于用户任务卸载决策和本地计算通信资源的联合优化问题，并结合深度学习技术和凸优化理论，提出了一种混合深度学习-优化算法对目标问题进行求解。仿真结果表明，相较于对比算法，所提算法能使用户的效用提升至少85.4%，并能在亚秒级的时间内实现用户效用的近似最优化。

关键词: 移动边缘计算, 效用最大化, 凸优化, 深度学习

Abstract:

In order to deal with the increasingly scarce computing resources, a cooperative edge computing scheme was proposed, which makes full use of the idle resources among users to improve the overall data processing performance.To maximize the user utility, the target problem was formulated as an MINLP (mixed integer non-linear programming), and a learning-optimization-integrated method was proposed to jointly optimize the resource allocation and user offloading decisions.Simulation results show that the proposed scheme can produce a near-optimal solution in sub-second and effectively improve the system utility at least 85.4% compared to the considered benchmark methods.

Key words: mobile edge computing, utility maximization, convex optimization, reinforcement learning

中图分类号:

TN92

李贤, 毕宿志, 曾泓儒, 林彬, 林晓辉. 基于智能化用户协作的边缘计算任务卸载与资源分配优化[J]. 物联网学报, 2022, 6(4): 41-52.

Xian LI, Suzhi BI, Hongru ZENG, Bin LIN, Xiaohui LIN. Collaborative task offloading and resource allocation optimization for intelligent edge devices[J]. Chinese Journal on Internet of Things, 2022, 6(4): 41-52.

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参考文献 11

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总任务数量/个	11	12	13	14	15
PSO算法计算时间/s	46.6	46.5	46.8	48.4	47.4
BCD算法计算时间/s	0.74	0.81	1.00	1.07	1.35
混合深度学习-优化算法计算时间/s	0.09	0.09	0.10	0.11	0.14

基于智能化用户协作的边缘计算任务卸载与资源分配优化

Collaborative task offloading and resource allocation optimization for intelligent edge devices

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