计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 280-288.doi: 10.11896/jsjkx.221100250

• 计算机网络 • 上一篇    下一篇

基于改进NSGA-III的D2D协同MEC多目标优化研究

王志鸿1, 王高才1, 赵启飞2   

  1. 1 广西大学计算机与电子信息学院 南宁530004
    2 广西大学电气工程学院 南宁530004
  • 收稿日期:2022-11-29 修回日期:2023-01-11 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: 王高才(wanggcgx@163.com)
  • 作者简介:(wangzhgx@foxmail.com)
  • 基金资助:
    :国家自然科学基金(62062007)

Multi-objective Optimization of D2D Collaborative MEC Based on Improved NSGA-III

WANG Zhihong1, WANG Gaocai1, ZHAO Qifei2   

  1. 1 School of Computer and Electronic Information,Guangxi University,Nanning 530004,China
    2 School of Electrical Engineering,Guangxi University,Nanning 530004,China
  • Received:2022-11-29 Revised:2023-01-11 Online:2024-03-15 Published:2024-03-13
  • About author:WANG Zhihong,born in 1999,postgraduate.His main research interests include computer network and mobile edge computing.WANG Gaocai,born in 1976,Ph.D,professor,doctoral supervisor.His main research interests include computer network,performance evaluation and network security.
  • Supported by:
    National Natural Science Foundation of China(62062007).

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摘要： 在当前的移动边缘计算(Mobile Edge Computing,MEC)模型中,由于任务是直接上传到MEC服务器执行,存在边缘服务器的计算压力大、空闲移动设备上的资源未得到充分利用等问题。使用边缘网络中的空闲设备进行协同计算,能够实现用户闲置资源的合理利用,增强MEC的计算能力。因此,提出了一种利用终端直通(Device-to-Device,D2D)进行协同计算的部分卸载MEC模型(D2D Collaborative MEC for Partial Offloading,DCM-PO)。在该模型中,除本地计算和MEC服务器计算外,还能将部分任务上传到空闲D2D设备进行辅助计算。首先,以最小化边缘网络的时延、能耗和费用为目标建立多目标优化问题。然后,在多染色体混合编码、自适应交叉率和变异率等方面对基于参考点的非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm III,NSGA-III)进行改进,使之适合DCM-PO模型中的多目标优化问题求解。最后,仿真结果表明,相比基准MEC模型,DCM-PO模型在多项性能指标上有明显优势。

关键词: 移动边缘计算, D2D, 任务卸载, 多目标优化, NSGA-III

Abstract: In the current mobile edge computing(MEC),since tasks are directly uploaded to the MEC server for execution,there are problems such as high computing pressure on the edge server and insufficient utilization of resources on idle mobile devices.Using idle devices in the edge network for collaborative computing can realize rational utilization of user's idle resources and enhance the computing capacity of MEC.Therefore,a device-to- device(D2D) collaborative MEC for partial offloading(DCM-PO) is proposed.In this model,in addition to local computing and MEC server computing,part of the tasks can be uploaded to idle D2D devices for auxiliary computing.First,a multi-objective optimization problem is established to minimize the delay,energy consumption and cost of the edge network.Then,the non-dominated sorting genetic algorithm III(NSGA-III) is improved in the aspects of multi-chromosome mixed coding,adaptive crossover rate and mutation rate,so that it is suitable for solving the multi-objective optimization problem in the DCM-PO.Finally,simulation results show that,compared with the baseline MEC,the DCM-PO has advantages in several performance indicators.

Key words: Mobile edge computing, Device-to-Device, Task offloading, Multi-objective optimization, NSGA-III

中图分类号: 

  • TP301
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