异构边缘云架构下的多任务卸载算法

尼俊红; 臧云

doi:10.11990/jheu.202111007

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异构边缘云架构下的多任务卸载算法

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- 异构边缘云架构下的多任务卸载算法
- Multitask offloading algorithm under heterogeneous edge cloud architecture
- 哈尔滨工程大学学报 2024年45卷第4期页码：800-807
- 作者机构：
  
  1.华北电力大学(保定) 电子与通信工程系, 河北保定 071003
  2.华北电力大学河北省电力物联网技术重点实验室, 河北保定 071003
- 作者简介：
  
  [ "尼俊红, 女, 副教授, 硕士生导师" ]
- 基金信息：
  
  国家自然科学基金项目(61771195);河北省自然科学基金项目(F2018502047)
- DOI：10.11990/jheu.202111007
  中图分类号： TN929.5
- 收稿：2021-11-04，
  
  网络首发：2024-01-31，
  
  纸质出版：2024-04-05
- 稿件说明：
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尼俊红, 臧云. 异构边缘云架构下的多任务卸载算法[J]. 哈尔滨工程大学学报, 2024,45(4):800-807.

Junhong NI, Yun ZANG. Multitask offloading algorithm under heterogeneous edge cloud architecture[J]. Journal of Harbin Engineering University, 2024, 45(4): 800-807.
尼俊红, 臧云. 异构边缘云架构下的多任务卸载算法[J]. 哈尔滨工程大学学报, 2024,45(4):800-807. DOI： 10.11990/jheu.202111007.

Junhong NI, Yun ZANG. Multitask offloading algorithm under heterogeneous edge cloud architecture[J]. Journal of Harbin Engineering University, 2024, 45(4): 800-807. DOI： 10.11990/jheu.202111007.

摘要

为在资源有限的终端设备上运行计算密集型与时延敏感型应用

同时降低系统时延和能耗

构建边缘云异构网络模型。本文提出了一种H-PSOGA多任务卸载优化算法

并通过无人机、路边单元、车辆等边缘设备以及边缘云服务器进行多任务计算卸载。该算法以先串行再并行的方式将粒子群和遗传算法结合在一起

通过适应度值排序、种群选择、多点交叉、反向变异等操作

利用遗传算法对粒子群进行优选

弥补粒子群算法早熟收敛、陷入局部最优的缺陷。6种标准测试函数的测试分析以及与基线方案进行仿真对比的结果表明: 在用户数较多时

混合优化算法的系统平均开销可降低26 %~43 %

可以有效提高收敛精度。

Abstract

To run computing-intensive and delay-sensitive applications on terminal devices with limited resources and to reduce system time delay and energy consumption

an edge cloud heterogeneous network model is constructed. In addition

a hierarchical particle swarm optimization with genetic algorithm (H-PSOGA) for multitasking offloading optimization is proposed for multitasking computational offloading through edge devices

such as drones

roadside units

and vehicles

as well as edge cloud servers. The H-PSOGA combines particle swarm and genetic algorithms in a serial and then parallel manner. The genetic algorithm is used to optimize the particle swarm through operations such as fitness value sequencing calculation

population selection

multipoint crossover

and reverse mutation to compensate for the defects in the particle swarm algorithm. These issues include premature convergence and local optima. The test analysis of six standard test functions and the result of simulation comparison with the baseline scheme show that with a large number of users

the average cost can be reduced by 26 % to 43 %

H-PSOGA can effectively improve convergence accuracy reduce system overhead.

关键词

Keywords

references

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