计算机集成制造系统 ›› 2023, Vol. 29 ›› Issue (7): 2277-2291.DOI: 10.13196/j.cims.2023.07.013

• • 上一篇    下一篇

面向时延和能耗联合优化的MEC计算卸载策略

杨火根1,杨忠明1,2,张先超2,宋逸杰1,2,戴亚盛2,3,黄淳岚1,2,乐光学1,2+   

  1. 1.江西理工大学理学院
    2.嘉兴学院浙江省医学电子与数字健康重点实验室
    3.上海大学计算机工程与科学学院
  • 出版日期:2023-07-31 发布日期:2023-08-09
  • 基金资助:
    国家自然科学基金资助项目(U19B2015,61941104,12161043);江西省自然科学基金资助项目(20192BAB201007);2021年江西省研究生创新专项资金资助项目(YC2021-S600)。

Joint optimization of delay and energy consumption computation offloading scheme for MEC

YANG Huogen1,YANG Zhongming1,2,ZHANG Xianchao2,SONG Yijie1,2,DAI Yasheng2,3,HUANG Chunlan1,2,YUE Guangxue1,2+#br#   

  1. 1.Collegeof Science,Jiangxi University of Science and Technology
    2.Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province,Jiaxing University
    3.School of Computer Engineering and Science,Shanghai University
  • Online:2023-07-31 Published:2023-08-09
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.U19B2015,61941104,12161043),the Natural Science Foundation of Jiangxi Province,China(No.20192BAB201007),and the Innovation Foundation for Postgraduate Students of Jiangxi Province in 2021,China(No.YC2021-S600).

摘要: 在移动边缘计算(MEC)中,计算卸载可以有效缓解资源受限和提高网络服务质量。以任务执行时延、终端能耗和边缘服务器负载率的联合优化为目标,提出面向时延和能耗联合优化的MEC计算卸载策略。构建多目标约束的成本优化模型,引入多变异算子,以迭代关联概率更新变异算子,设计多变异差分进化(MDE)算法求解,实现计算卸载成本最优。为验证MDE算法的有效性,基于Autonomous Systems by Skitter公开数据集构建3个不同规模的实验网络,将MDE算法与随机计算卸载算法、能量优化计算卸载算法、多目标贪婪计算卸载等算法进行对比分析,MDE算法的执行成功率、卸载成功率、服务器负载均衡性分别平均提升了13.23%,12.96%,29.37%,MDE算法能实现MEC中高效、稳定的计算卸载。

关键词: 移动边缘计算, 时延与能耗, 负载均衡, 多变异差分算法, 计算卸载

Abstract: In Mobile Edge Computing (MEC),computation offloading can effectively alleviate resource constraints and improve network quality of service.A joint optimization of delay and energy consumption computation offloading scheme for MEC was proposed with the joint optimization of task delay,terminal energy consumption and edge server load rate.A cost optimization model with multi-objective constraints was constructed,and a Multi-Mutation Differential Evolution (MDE) algorithm was designed by introducing the multi-mutation operator that was updated with iterative correlation probability to solve.To verify the effectiveness of MDE algorithm,three different scale experimental networks were constructed based on Autonomous Systems by Skitter public dataset.Compared with random computation offloading scheme,energy optimization computation offloading scheme and multi-objective greedy computation offloading scheme,MDE algorithm improved the execution success rate,offloading success rate and server load balancing by 13.23%,12.96%,29.37% respectively,which could realize efficient and stable computation offloading in MEC.

Key words: mobile edge computing, delay and energy consumption, load balance, multi-mutation differential algorithm, computation offloading

中图分类号: