Efficient Task Offloading Based on Traffic Prediction in IoV-Enabled Edge Computing

XU Xiao-long; YANG Wei; YANG Chen-yi; CHENG Yong; QI Lian-yong; XIANG Hao-long; DOU Wan-chun

doi:10.12263/DZXB.20240609

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Efficient Task Offloading Based on Traffic Prediction in IoV-Enabled Edge Computing

Cloud-Edge-Device Collaborative Data Management, Analysis and System | 更新时间：2025-12-08

- Efficient Task Offloading Based on Traffic Prediction in IoV-Enabled Edge Computing
- ACTA ELECTRONICA SINICA Vol. 53, Issue 2, Pages: 329-343(2025)
- 作者机构：
  
  1.南京信息工程大学软件学院，江苏南京 210044
  2.江苏省大气环境与装备技术协同创新中心，江苏南京 210044
  3.江苏省先进计算与智能服务工程研究中心，江苏南京 210044
  4.东南大学软件学院，江苏南京 211189
  5.中国石油大学（华东）计算机科学与技术学院，山东青岛 266580
  6.南京大学计算机软件新技术国家重点实验室，江苏南京 210000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62372242;92267104)
- DOI：10.12263/DZXB.20240609
  CLC： TN929.5;
- Received：01 July 2024，
  
  Revised：2024-11-19，
  
  Published：25 February 2025
- 稿件说明：
移动端阅览
许小龙, 杨威, 杨辰翊, 等. 车联网边缘计算环境下基于流量预测的高效任务卸载策略研究[J]. 电子学报, 2025, 53(02): 329-343.

XU Xiao-long, YANG Wei, YANG Chen-yi, et al. Efficient Task Offloading Based on Traffic Prediction in IoV-Enabled Edge Computing[J]. Acta Electronica Sinica, 2025, 53(02): 329-343.
许小龙, 杨威, 杨辰翊, 等. 车联网边缘计算环境下基于流量预测的高效任务卸载策略研究[J]. 电子学报, 2025, 53(02): 329-343. DOI：10.12263/DZXB.20240609

XU Xiao-long, YANG Wei, YANG Chen-yi, et al. Efficient Task Offloading Based on Traffic Prediction in IoV-Enabled Edge Computing[J]. Acta Electronica Sinica, 2025, 53(02): 329-343. DOI：10.12263/DZXB.20240609

摘要

车联网（Internet of Vehicles，IoV）边缘计算通过将移动边缘计算和车联网相结合，实现了车辆计算任务从云服务器向边缘服务器的下沉，从而有效降低了车联网服务的响应时延.然而，车联网中不规则的交通流时空分布会导致边缘服务器计算负载不均衡，进而影响车联网服务的实时响应.为此，本文提出了一种车联网边缘计算环境下基于流量预测的高效任务卸载策略.具体而言，首先设计了能充分挖掘路段间连通性和距离信息的切比雪夫图加权网络（Chebyshev graph Weighted Network，ChebWN）进行交通流量预测.然后，设计了一种基于深度强化学习的二元任务卸载方法（DRL-based Binary task Offloading Algorithm，DBOA），该算法将二元任务卸载的决策过程分为两个阶段，即首先通过深度强化学习得到卸载策略，再通过一维双端查找算法确定最大化总计算速率的时间片分配方案，降低了决策过程的复杂度.最后，通过大量的对比实验验证了ChebWN在预测交通流量方面的准确性，以及DBOA在提升车联网服务响应速度方面的优越性.

Abstract

Vehicle edge computing combines mobile edge computing and the internet of vehicles(IoV) to offload the vehicle computing tasks from the cloud servers to edge servers

which effectively reduces the response time of IoV services. However

the irregular spatiotemporal distributions of traffic flows in vehicle networking will lead to the imbalance of computing load on the edge servers

which impacts real-time responsiveness of vehicle networking services. To address this issue

this paper proposes an efficient task offloading strategy based on traffic prediction in the vehicle edge computing. Specifically

a chebyshev graph weighted network (ChebWN) is designed to forecast traffic flow by fully leveraging connectivity and distance information between road segments. Next

a deep reinforcement learning-based binary task offloading algorithm (DBOA) is designed

which divides the binary task offloading decision process into two stages. Initially

a deep reinforcement learning approach is employed to derive the offloading strategies. Subsequently

a one-dimensional bi-end search algorithm is utilized to determine the time slot allocation scheme that maximizes the overall computation rate

thereby reducing the complexity of the decision-making process. Finally

a large number of comparative experiments demonstrate the accuracy of ChebWN in predicting traffic flow and the superiority of DBOA in improving the response speed of vehicle services.

关键词

Keywords

references

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