边云协作下时延和能耗约束的启发式任务卸载方法

doi:10.13229/j.cnki.jdxbgxb.20230849

吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (5): 1648-1663.doi: 10.13229/j.cnki.jdxbgxb.20230849

边云协作下时延和能耗约束的启发式任务卸载方法

苏命峰^1,²(),王国军³(),周聪¹,王田⁴

^1.湖南第一师范学院计算机学院，长沙 410205
^2.中南大学计算机学院，长沙 410083
^3.广州大学计算机科学与网络工程学院，广州 510006
^4.北京师范大学人工智能与未来网络研究院，广东珠海 519087

收稿日期:2023-08-11 出版日期:2025-05-01 发布日期:2025-07-18
通讯作者: 王国军 E-mail:mfsu@hnfnu.edu.cn;csgjwang@gzhu.edu.cn
作者简介:苏命峰（1980-），男，教授，博士. 研究方向：边缘计算与协同计算. E-mail：mfsu@hnfnu.edu.cn
基金资助:
国家自然科学基金项目(62372121);国家重点研发计划项目(2020YFB1005804);湖南省自然科学基金项目(2024JJ5105);湖南省自然科学基金项目(2023JJ40081);中南大学中央高校基本科研业务费专项项目(2018zzts180)

A heuristic task offloading approach with delay and energy constraints for edge-cloud collaboration

Ming-feng SU^1,²(),Guo-jun WANG³(),Cong ZHOU¹,Tian WANG⁴

^1.School of Computer Science，Hunan First Normal University，Changsha 410205，China
^2.School of Computer Science and Engineering，Central South University，Changsha 410083，China
^3.School of Computer Science and Cyber Engineering，Guangzhou University，Guangzhou 510006，China
^4.Institute of Artificial Intelligence and Future Networks，Beijing Normal University，Zhuhai 519087，China

Received:2023-08-11 Online:2025-05-01 Published:2025-07-18
Contact: Guo-jun WANG E-mail:mfsu@hnfnu.edu.cn;csgjwang@gzhu.edu.cn

摘要/Abstract

摘要：

为解决移动边缘计算中设备资源受限叠加任务复杂变化，引起负载失衡、任务时延和能耗增大等问题，仿生麻雀共生合作觅食搜索提出一种时延和能耗约束的边云协作计算任务卸载方法。首先，适应移动边云协作，设计飞行者改进发现者更新、正余弦扰动跟随者更新和自适应调整预警者更新，提出一种多策略改进麻雀搜索算法（MSSA）优化任务卸载位置。然后，考虑任务最大完成期限与时延松弛变量，融入超时惩罚能耗，提出一种基于MSSA的启发式任务卸载算法（HTMA），贪心比较不同时延约束下预卸载位置集的总任务时延和总任务能耗，进一步优化任务卸载。仿真实验表明：相比同类算法，本文搜索算法能有效提升寻优精度、收敛速度和鲁棒性，并且本文任务卸载算法适应网络变化的任务平均时延、总任务能耗和节点负载均衡度性能更优。

关键词: 边缘计算, 任务卸载, 边云协作, 启发式算法, 云计算

Abstract:

To address the problems of load imbalance， task delay， and increased energy consumption caused by limited device resources and complex task variations in mobile edge computing， a computing task offloading approach with delay and energy constraints for edge-cloud collaboration is proposed， inspired by the cooperative foraging search of sparrow populations. Firstly， adapting to mobile edge cloud collaboration， designing the flyer improved producer update， sine-cosine perturbed follower update， and adaptively adjusted alerter update， a multi-strategy improved sparrow search algorithm （MSSA） is proposed to optimize task offloading location. Then， considering the task maximum completion deadline and delay relaxation variables， incorporating the timeout penalty energy consumption， a heuristic task offloading with MSSA algorithm （HTMA） is proposed， which greedily compares the total task delay and total task energy consumption of pre-offloading location sets under different delay constraints to further optimize task offloading. Experimental results show that compared with similar algorithms， MSSA can improve the optimization accuracy， convergence speed， and robustness of location search. Moreover， HTMA adapts to network changes with better performance of average task completion delay， total task energy consumption， and node load balancing degree.

Key words: edge computing, task offloading, edge-cloud collaboration, heuristic algorithm, cloud computing

中图分类号:

TP301.6

苏命峰,王国军,周聪,王田. 边云协作下时延和能耗约束的启发式任务卸载方法[J]. 吉林大学学报(工学版), 2025, 55(5): 1648-1663.

Ming-feng SU,Guo-jun WANG,Cong ZHOU,Tian WANG. A heuristic task offloading approach with delay and energy constraints for edge-cloud collaboration[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1648-1663.

图/表 18

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图2

图3

表1

表2

算法参数设置"

算法	参数
MSSA	$μ s d$ =20， $? w v$ =0.8， $μ s w m a x$ =30， $μ s w m i n$ =3， n（j）=100
SSA	$μ s d$ =20， $? w v$ =0.8， $μ s w$ =20， n（j）=100
RWSSA	$μ s d$ =20， $? w v$ =0.8， $μ s w$ =20， n（j）=100
AWPSO	C₁=1.5， C₂=1.5， W=0.8， n（j）=100

表2

图4

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图8

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参考文献 22

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参数	单位	值范围
任务数据量	kB	［600，1 000］
任务计算量	cycle·kbit^-1	［20，100］
任务最大完成期限	ms	50
无线传输功率	W	［2，5］
边缘设备的最大无线链路带宽	Mbit/s	100
信道增益系数^［11］		8.2^-8~1.5^-6
噪声功率	W	［1，2］?10^-8
边缘设备的最大算力	GHz	2×［2，4］
边缘设备的有线链路带宽	Mbit/s	1 000
边缘设备的有线传输功率	W	［1，3］
边缘设备任务执行功率系数	W·Hz^-3	［1.2，1.5］×10^-26
云中心任务执行功率	W	［2.1，2.5］×10^-25

边云协作下时延和能耗约束的启发式任务卸载方法

A heuristic task offloading approach with delay and energy constraints for edge-cloud collaboration

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