计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (8): 354-362.doi: 10.11896/jsjkx.240700088

• 计算机网络 • 上一篇    下一篇

基于异构节点的高效任务卸载策略

范兴刚1, 姜新阳1, 谷文婷1, 徐骏涛1, 杨友东2, 李强1   

  1. 1 浙江工业大学计算机科学与技术学院 杭州 310023
    2 浙江工业大学之江学院 浙江 绍兴 312030
  • 收稿日期:2024-07-15 修回日期:2024-10-16 出版日期:2025-08-15 发布日期:2025-08-08
  • 通讯作者: 杨友东(yydong@zjut.edu.cn)
  • 作者简介:(xgfan@zjut.edu.cn)
  • 基金资助:
    浙江省“尖兵”研发攻关计划(2023C01029,2025C01054)

Effective Task Offloading Strategy Based on Heterogeneous Nodes

FAN Xinggang1, JIANG Xinyang1, GU Wenting1, XU Juntao1, YANG Youdong2, LI Qiang1   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 Zhijiang College of Zhejiang University of Technology,Shaoxing,Zhejiang 312030,China
  • Received:2024-07-15 Revised:2024-10-16 Online:2025-08-15 Published:2025-08-08
  • About author:FAN Xinggang,born in 1974,Ph.D,professor,is a member of CCF(No.74204M).His main research interests include sensor networks,network communication and Internet of Things.
    YANG Youdong,born in 1970,Ph.D,professor.His main research interests include computer-aided design and graphics and so on.
  • Supported by:
    “Pioneer” and “Leading Goose” R&D Program of Zhejiang(2023C01029,2025C01054).

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摘要： 在车联网边缘计算中,如何利用有限的网络资源实施高效的任务卸载,是近年来车联网的研究热点。通过研究异构节点模式下的任务卸载,设计了一种异构节点模式下的高效任务卸载策略TOS-HN。当车辆产生任务时,优先考虑移动节点卸载,将任务卸载到附近空闲车辆上。若移动卸载不能满足任务需求,则采用固定节点卸载策略。在移动节点卸载模式中,先根据任务处理时延和能耗构建代价矩阵,再通过匈牙利算法确定任务车辆和处理车辆的最优匹配。仿真实验证明,TOS-HN算法相比于其他算法具有显著优势,在时延、能耗、任务成功率和基站负载方面均具有较好的性能。

关键词: 车联网边缘计算, 任务卸载, 异构节点模式, 移动, 代价矩阵

Abstract: In vehicular edge computing(VEC),how to use the limited network resources to implement efficient task unloading is a research hotspot in recent years.This paper focuses on task offloading in the heterogeneous node mode and designs an efficient task offloading strategy in heterogeneous node mode(TOS-HN).When a vehicle generates a task,mobile node offloading is prio-ritized to offload the task to a nearby idle vehicle.If the mobile offloading cannot meet the task requirements,a fixed node offloa-ding strategy is adopted.In the mobile node offloading mode,the cost matrix is first constructed based on the task processing delay and energy consumption,and then the Hungarian algorithm is used to determine the optimal matching between the task vehicle and the processing vehicle.Simulation experiment proves that the TOS-HN algorithm has significant advantages over other algorithms,with better performance in terms of delay,energy consumption,task success rate and base station load.

Key words: Vehicular edge computing, Task offloading, Heterogeneous node mode, Mobile, Cost matrix

中图分类号: 

  • TP393
[1]FAN X G,GU W T,LONG C Q,et al.Optimizing Task Offloading and Resource Allocation in Vehicular Edge Computing Based on Heterogeneous Cellular Networks[J].IEEE Transactions on Vehicular Technology,2023,73(5):7175-7187.
[2]LIN X Y,YAO Z W,HU S X,et al Task Offloading Algorithm Based on Federated Deep Reinforcement Learning for Internet of Vehicles[J].Computer Science,2023,50(9):347-356.
[3]ZHANG H B,ZHANG Y F,LIU K J.Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC[J].Computer Science,2022,49(2):304-311.
[4]MENEGUETTE R,DE G R,UEYAMA J,et al.Vehicular edge computing:Architecture,resource management,security,and challenges[J].ACM Computing Surveys,2021,55(1):1-46.
[5]HE S,SHI K,LIU C,et al.Collaborative sensing in internet of things:A comprehensive survey[J].IEEE Communications Surveys & Tutorials,2022,24(3):1435-1474.
[6]TANG C G,LI Z,XIAO S,et al.A Service Caching-Based Task Collaborative Offloading Algorithm for Vehicular Edge Computing[J].Chinese Journal of Computers,2025,48(4):864-876.
[7]ZHU S F,HU J M,YANG C R,et al.Optimization of offloading decision based on priority task in edge computing scenes of internet of things[J].Journal of Jilin University(Engineering and Technology Edition),2024,54(11):3338-3350.
[8]TANG L,TANG B,ZHANG L,et al.Joint optimization of network selection and task offloading for vehicular edge computing[J].Journal of Cloud Computing,2021,10(1):23.
[9]LEE S,LEE S K.Resource allocation for vehicular fog computing using reinforcement learning combined with heuristic information[J].IEEE Internet of Things Journal,2020,7(10):10450-10464.
[10]ZHOU H,JIANG K,LIU X,et al.Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing[J].IEEE Internet of Things Journal,2021,9(2):1517-1530.
[11]ZHANG Z,WANG N,WU H,et al.MR-DRO:A fast and efficient task offloading algorithm in heterogeneous edge/cloud computing environments[J].IEEE Internet of Things Journal,2021,10(4):3165-3178.
[12]XU L,LIU Y,FAN B,et al.An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority[J].Electronics,2024,13(3):540.
[13]SUN Y,WU Z,MENG K,et al.Vehicular Task Offloading and Job Scheduling Method Based on Cloud-Edge Computing[J].IEEE Transactions on Intelligent Transportation Systems,2023,24(12):14651-14662.
[14]ZHAO X,LIU M,LI M.Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning[J].Ad Hoc Networks,2023,147:103193.
[15]ZHU C,TAO J,PASTOR G,et al.Folo:Latency and quality optimized task allocation in vehicular fog computing[J].IEEE Internet of Things Journal,2018,6(3):4150-4161.
[16]QIAO B,LIU C,LIU J,et al.Task migration computation offloading with low delay for mobile edge computing in vehicular networks[J].Concurrency and Computation:Practice and Experience,2022,34(1):e6494.
[17]ZHANG R,WU L,CAO S,et al.Task offloading with task classification and offloading nodes selection for MEC-enabled IoV[J].ACM Transactions on Internet Technology,2021,22(2):1-24.
[18]ZHANGD G,YAN H R,ZHANG J.ApproxECIoT:New Edge Computing Architecture Based on Adaptive Stratified Sampling[J].Journal of Software,2022,33(9):3437-3452.
[19]JANG Y,NA J,JEONG S,et al.Energy-efficient task offloading for vehicular edge computing:Joint optimization of offloading and bit allocation[C]//2020 IEEE 91st Vehicular Technology Conference.IEEE,2020:1-5.
[20]LI S,ZHANG N,JIANG R,et al.Joint task offloading and resource allocation in mobile edge computing with energy harvesting[J].Journal of Cloud Computing,2022,11(1):17.
[21]LU Y,LUO M X,WANG X.Large-scale mobile edge computing with joint offloading decision and resource allocation[C]//International Conference on Artificial Intelligence and Security.Cham:Springer,2022:271-286.
[22]WANG L,ZHANG J H,WANG T,et al.Fine-grained multi-access edge computing architecture for cloud network integration [J].Journal of Computer Research and Development,2021,58(6):1275-1290.
[23]ZENG F,CHEN Q,MENG L,et al.Volunteer assisted collaborative offloading and resource allocation in vehicular edge computing[J].IEEE Transactions on Intelligent Transportation Systems,2020,22(6):3247-3257.
[24]MEN R,FAN X,SHAN A,et al.Fuzzy Logic based BinaryComputation Offloading Scheme in V2X Communication Networks[J].IEEE Access,2024,12:45507-45518.
[25]HUANG J,QIAN Y,HU R Q.A vehicle-assisted data offloa-ding in mobile edge computing enabled vehicular networks[C]//2019 IEEE Global Communications Conference.IEEE,2019:1-6.
[26]RAZA S,WANG S,AHMED M,et al.Task offloading and resource allocation for IoV using 5G NR-V2X communication[J].IEEE Internet of Things Journal,2021,9(13):10397-10410.
[27]FAN W,SU Y,LIU J,et al.Joint task offloading and resource allocation for vehicular edge computing based on V2I and V2V modes[J].IEEE Transactions on Intelligent Transportation Systems,2023,24(4):4277-4292.
[28]WANG H Y,HUANG Q,et al.Graph Theory Algorithm and its MATLAB Implementation [M].Beijing:Beihang University Press,2010.
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