广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (05): 73-80.doi: 10.12052/gdutxb.220150

• 综合研究 • 上一篇    

多用户多时隙移动边缘计算系统的计算缓存优化设计

梁静轩, 王丰   

  1. 广东工业大学 信息工程学院, 广东 广州 510006
  • 收稿日期:2022-09-30 发布日期:2023-09-26
  • 通信作者: 王丰(1987-),男,副教授,博士,主要研究方向为无线通信系统、移动边缘计算资源管理等,E-mail:fengwang13@gdut.edu.cn
  • 作者简介:梁静轩(1995-),男,硕士研究生,主要研究方向为移动边缘计算缓存
  • 基金资助:
    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)

Optimized Design for Multiuser Cache-enabled Mobile Edge Computing

Liang Jing-xuan, Wang Feng   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-09-30 Published:2023-09-26

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摘要： 在动态环境下，移动边缘计算(Mobile Edge Computing, MEC) 系统的节能缓存策略和计算卸载设计面临着“双随机性”难题，移动边缘服务器的缓存决策需要同时与时变的无线信道状态和随机达到的用户任务相适应。为此，本文建模多用户多时隙移动边缘计算系统的计算缓存和计算任务处理模型，建立MEC缓存容量、计算任务因果性和任务完成时限约束模型。系统模型以最小化加权能耗和为设计目标，联合优化MEC服务器缓存决策和任务计算量以及无线设备的本地计算量和计算卸载量。所提优化问题属于一类NP难问题，为求解该优化问题，首先提出基于分支定界算法的最优设计方案作为其他实用方案的性能下界。为降低计算复杂度，提出一种基于凸松弛的算法方案，该算法方案能取得系统性能和计算复杂度的良好折中。仿真结果表明，基于凸松弛的算法方案逼近基于分支定界法的最优性能曲线并优于本文考虑的基准方案。

关键词: 移动边缘计算, 计算卸载, 计算任务缓存, 凸松弛, 分支定界法

Abstract: The energy-efficient caching strategy and computation offloading design of mobile edge computing (MEC) faces the double randomness challenges, which require to adapt to the time-varying wireless channel state and the dynamic task arrivals. This paper investigates a cache-enabled mobile edge computing system with dynamic tasks arriving at multiple wireless devices. By minimizing the system weighted sum energy over multiple time slots, we optimize the AP's task caching decision and MEC execution, the wireless devices’ local computing and tasks offloading under the caching capacity and computation causality, and the computation deadline constraints. The branch-and-bound (BnB) method is first presented to obtain the globally optimal solution to define the lower bound for practical schemes. Then, a relaxation-based scheme is proposed to efficiently achieve a near-optimal solution. Numerical results show that the proposed relaxation-based scheme achieves a closer performance to the optimal BnB scheme when compared to the benchmark schemes.

Key words: mobile edge computing, computation offloading, tasks caching, convex relaxation, Branch-and-Bound method

中图分类号: 

  • F224.32
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