资源受限环境下的智能终端任务卸载方法

doi:10.3778/j.issn.1002-8331.2410-0122

摘要/Abstract

摘要： 针对一般的移动边缘计算（mobile edge computing，MEC）模式在无边缘服务器情况下资源紧张，计算需求得不到满足的问题，研究智能终端（smart terminal，ST）资源和计算任务建模方法，并设计启发式算法生成任务卸载方案。提出轻量级的计算任务-ST资源匹配模型，让ST间的任务交互不再依赖消耗资源较多的虚拟化技术。通过挑选合适的ST充当边缘节点，整合所有ST的资源及任务信息，并设计基于最佳亲本保留的分组遗传算法（grouped genetic algorithm based on best-parent preservation，GGABP），优化多个ST以相互卸载方式完成所有任务的效率。仿真结果表明，GGABP的稳定性、快速性、可拓展性均优于对比算法，所提方法能充分利用ST的资源并满足其任务卸载需求。

关键词: 物联网, 边缘计算, 任务卸载, 资源管理, 启发式算法

Abstract: Aiming at the problem that when there are no edge servers, the general mobile edge computing (MEC) mode is short of resources that cannot cover the computing requirements, a modeling method of smart terminal (ST) resources and computing tasks is proposed with a heuristic algorithm that can generate a task offloading scheme. Firstly, a lightweight resource model of computing tasks and ST resources is proposed, which makes task interactions between STs no longer rely on resource-intensive virtualization techniques. Then integrating the resources and task information of STs through selecting appropriate ST as an edge node, and the grouped genetic algorithm based on best parent preservation (GGABP) is designed to derive task offloading strategies, allowing STs to offload tasks with each other to complete all tasks as soon as possible. The final simulation results show that GGABP has better stability, speed, and scalability comparing to other algorithms. The proposed method can fully utilize the resources of STs and meet the task offloading requirements.

Key words: Internet of things (IoT), mobile edge computing (MEC), task offloading, resource management, heuristic algorithms

陈学硕, 毛玉星, 徐宜航, 杨文超, 雷波铮. 资源受限环境下的智能终端任务卸载方法[J]. 计算机工程与应用, 2025, 61(24): 293-301.

CHEN Xueshuo, MAO Yuxing, XU Yihang, YANG Wenchao, LEI Bozheng. Smart Terminal Task Offloading Method in Resource-Constrained Environments[J]. Computer Engineering and Applications, 2025, 61(24): 293-301.

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