{"title":"基于多智能体的车辆边缘计算在线协同计算卸载与迁移策略","authors":"Yuya Cui, Hao Qiang, Honghu Li, Haitao Zhao","doi":"10.1049/itr2.70083","DOIUrl":null,"url":null,"abstract":"<p>Vehicular edge computing (VEC) has emerged as a promising paradigm to reduce the latency of vehicular tasks by leveraging edge computing resources. However, the high mobility of vehicles and the limited computational capacity of edge servers (ESs) present significant challenges to achieving efficient VEC. To address these challenges, this paper proposes a fine-grained computation task cooperative offloading and migration strategy. Specifically, applications are decomposed into multiple interdependent subtasks, which are collaboratively executed across multiple ESs. As vehicles move, computation tasks are dynamically migrated among ESs to ensure service continuity. The joint optimisation of task offloading and migration is formulated as a multi-stage mixed integer non-linear programming problem. To tackle this problem, we first employ Lyapunov optimisation to transform the multi-stage problem into a deterministic optimisation problem at each time slot, aiming to maximise long -term system revenue. Furthermore, considering the dynamic environment characterised by vehicle mobility, time-varying channels, subtask dependencies and inter-vehicle channel interference, we integrate a graph convolutional network (GCN) into the counterfactual multi-agent policy gradients (COMA) framework. By integrating Lyapunov optimisation with COMA-GCN, we propose Ly-COMA, a novel algorithm that effectively minimises the average task execution delay. Extensive experimental results demonstrate that the proposed algorithm outperforms existing methods in terms of average delay reduction and migration cost efficiency.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70083","citationCount":"0","resultStr":"{\"title\":\"Multi-Agent Based Online Cooperative Computation Offloading and Migration Strategy for Vehicular Edge Computing\",\"authors\":\"Yuya Cui, Hao Qiang, Honghu Li, Haitao Zhao\",\"doi\":\"10.1049/itr2.70083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Vehicular edge computing (VEC) has emerged as a promising paradigm to reduce the latency of vehicular tasks by leveraging edge computing resources. However, the high mobility of vehicles and the limited computational capacity of edge servers (ESs) present significant challenges to achieving efficient VEC. To address these challenges, this paper proposes a fine-grained computation task cooperative offloading and migration strategy. Specifically, applications are decomposed into multiple interdependent subtasks, which are collaboratively executed across multiple ESs. As vehicles move, computation tasks are dynamically migrated among ESs to ensure service continuity. The joint optimisation of task offloading and migration is formulated as a multi-stage mixed integer non-linear programming problem. To tackle this problem, we first employ Lyapunov optimisation to transform the multi-stage problem into a deterministic optimisation problem at each time slot, aiming to maximise long -term system revenue. Furthermore, considering the dynamic environment characterised by vehicle mobility, time-varying channels, subtask dependencies and inter-vehicle channel interference, we integrate a graph convolutional network (GCN) into the counterfactual multi-agent policy gradients (COMA) framework. By integrating Lyapunov optimisation with COMA-GCN, we propose Ly-COMA, a novel algorithm that effectively minimises the average task execution delay. Extensive experimental results demonstrate that the proposed algorithm outperforms existing methods in terms of average delay reduction and migration cost efficiency.</p>\",\"PeriodicalId\":50381,\"journal\":{\"name\":\"IET Intelligent Transport Systems\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70083\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Intelligent Transport Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.70083\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Intelligent Transport Systems","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.70083","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Multi-Agent Based Online Cooperative Computation Offloading and Migration Strategy for Vehicular Edge Computing
Vehicular edge computing (VEC) has emerged as a promising paradigm to reduce the latency of vehicular tasks by leveraging edge computing resources. However, the high mobility of vehicles and the limited computational capacity of edge servers (ESs) present significant challenges to achieving efficient VEC. To address these challenges, this paper proposes a fine-grained computation task cooperative offloading and migration strategy. Specifically, applications are decomposed into multiple interdependent subtasks, which are collaboratively executed across multiple ESs. As vehicles move, computation tasks are dynamically migrated among ESs to ensure service continuity. The joint optimisation of task offloading and migration is formulated as a multi-stage mixed integer non-linear programming problem. To tackle this problem, we first employ Lyapunov optimisation to transform the multi-stage problem into a deterministic optimisation problem at each time slot, aiming to maximise long -term system revenue. Furthermore, considering the dynamic environment characterised by vehicle mobility, time-varying channels, subtask dependencies and inter-vehicle channel interference, we integrate a graph convolutional network (GCN) into the counterfactual multi-agent policy gradients (COMA) framework. By integrating Lyapunov optimisation with COMA-GCN, we propose Ly-COMA, a novel algorithm that effectively minimises the average task execution delay. Extensive experimental results demonstrate that the proposed algorithm outperforms existing methods in terms of average delay reduction and migration cost efficiency.
期刊介绍:
IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following:
Sustainable traffic solutions
Deployments with enabling technologies
Pervasive monitoring
Applications; demonstrations and evaluation
Economic and behavioural analyses of ITS services and scenario
Data Integration and analytics
Information collection and processing; image processing applications in ITS
ITS aspects of electric vehicles
Autonomous vehicles; connected vehicle systems;
In-vehicle ITS, safety and vulnerable road user aspects
Mobility as a service systems
Traffic management and control
Public transport systems technologies
Fleet and public transport logistics
Emergency and incident management
Demand management and electronic payment systems
Traffic related air pollution management
Policy and institutional issues
Interoperability, standards and architectures
Funding scenarios
Enforcement
Human machine interaction
Education, training and outreach
Current Special Issue Call for papers:
Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf
Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf
Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf