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

IF 2.5 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yuya Cui, Hao Qiang, Honghu Li, Haitao Zhao
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引用次数: 0

摘要

车辆边缘计算(VEC)已经成为利用边缘计算资源来减少车辆任务延迟的一种有前途的范例。然而,车辆的高移动性和边缘服务器(ESs)有限的计算能力对实现高效VEC提出了重大挑战。为了解决这些问题,本文提出了一种细粒度计算任务协同卸载和迁移策略。具体来说,应用程序被分解为多个相互依赖的子任务,这些子任务跨多个ESs协作执行。随着车辆的移动,计算任务在ESs之间动态迁移,保证业务的连续性。将任务卸载与迁移的联合优化问题表述为一个多阶段混合整数非线性规划问题。为了解决这个问题,我们首先采用李亚普诺夫优化将多阶段问题转化为每个时隙的确定性优化问题,旨在最大化长期系统收益。此外,考虑到车辆移动性、时变通道、子任务依赖性和车辆间通道干扰等特征的动态环境,我们将图卷积网络(GCN)集成到反事实多智能体策略梯度(COMA)框架中。通过将Lyapunov优化与COMA-GCN相结合,我们提出了一种有效地最小化平均任务执行延迟的新算法Ly-COMA。大量的实验结果表明,该算法在平均延迟降低和迁移成本效率方面优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multi-Agent Based Online Cooperative Computation Offloading and Migration Strategy for Vehicular Edge Computing

Multi-Agent Based Online Cooperative Computation Offloading and Migration Strategy for Vehicular Edge Computing

Multi-Agent Based Online Cooperative Computation Offloading and Migration Strategy for Vehicular Edge Computing

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.

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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
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
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