移动边缘计算系统的双服务器协同与计算通信资源联合优化

李宇龙; 梁静轩; 王丰

doi:10.12052/gdutxb.220152

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移动边缘计算系统的双服务器协同与计算通信资源联合优化

综合研究 | 更新时间：2026-05-27

- 移动边缘计算系统的双服务器协同与计算通信资源联合优化
- Optimized Design and Resource Allocation for Dual-server Mobile Edge Computing Systems
- 广东工业大学学报 2024年41卷第1期页码：93-100
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  李宇龙(1999–) ，男，硕士研究生，主要研究方向为移动边缘计算资源管理
  王丰(1987–) ，男，副教授，主要研究方向为通信信号处理、移动边缘计算资源管理等，E-mail ：fengwang13@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61901124);广东省自然科学基金资助项目(2021A1515012305);广州市科技计划项目(202102020856)
- DOI：10.12052/gdutxb.220152
  中图分类号： TN929.5
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
李宇龙, 梁静轩, 王丰. 移动边缘计算系统的双服务器协同与计算通信资源联合优化[J]. 广东工业大学学报, 2024, 41(1): 93-100.

Li Yu-long, Liang Jing-xuan, Wang Feng. Optimized Design and Resource Allocation for Dual-server Mobile Edge Computing Systems[J]. 2024, 41(1): 93-100.
李宇龙, 梁静轩, 王丰. 移动边缘计算系统的双服务器协同与计算通信资源联合优化[J]. 广东工业大学学报, 2024, 41(1): 93-100. DOI： 10.12052/gdutxb.220152.

Li Yu-long, Liang Jing-xuan, Wang Feng. Optimized Design and Resource Allocation for Dual-server Mobile Edge Computing Systems[J]. 2024, 41(1): 93-100. DOI： 10.12052/gdutxb.220152.

摘要

为充分利用移动边缘计算(Mobile Edge Computing

MEC) 系统的计算资源，本文设计了双MEC服务器协同与计算通信资源联合优化方案。通过建模双服务器协同多用户任务计算优化问题，以系统计算时延和用户能耗的加权和最小化为准则，优化设计多用户计算卸载发射功率和计算任务分割。提出一种较低计算复杂度的联合设计方案，将原问题解耦为计算卸载优化和计算任务分割设计的2个子问题，分别通过内点法和单纯形法实现快速数值求解。仿真结果表明，本文所提算法的系统性能优于已有启发式基准算法方案，且在较少的算法运行时间下，联合优化算法方案能取得与最优拉格朗日乘子法基准方案相同的系统性能。

Abstract

In order to make full use of the computing resources of Mobile Edge Computing (MEC) system

this paper designs an optimization scheme of collaboration between two MEC servers and joint computing and communication resources. The scheme proposes the optimization problem of dual-server collaborative multi-user task calculation

where the weighted sum between system computing delay and user energy consumption is minized. The multi-user computing unloaded transmitting power and task segmentation are optimized in the proposed scheme. A joint design scheme with low computational complexity is proposed. The original problem is decoupled into two sub-problems of computational offload optimization and computational task segmentation design

both of which can be solved by interior point method and simplex method respectively. The simulation results show that the system performance of the proposed scheme is better than the existing heuristic benchmark algorithm scheme. And the joint optimization algorithm scheme can get the similar system performance as compared with the basic scheme of the optimal Lagrange multiplier method with less computation time.

关键词

Keywords

references

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