利用基于梯度联合学习的深度确定性策略为多接入边缘计算分配资源

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-06-27 DOI:10.1007/s10723-024-09774-2
Zheyu Zhou, Qi Wang, Jizhou Li, Ziyuan Li
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引用次数: 0

摘要

这项研究的重点是利用车辆中的计算资源来提高多访问边缘计算(MEC)系统的性能。虽然车辆通常都配备了计算服务,用于以车辆为中心的车联网(IoV)应用,但在多访问边缘计算(MEC)场景中,也可以利用车辆资源来减少边缘服务器的工作量,提高任务处理速度。以往的研究工作忽略了过往车辆的潜在资源利用率,而这些车辆与停放的汽车一样,可以成为 MEC 系统的宝贵补充。本研究介绍了一种辅助 MEC 场景,在该场景中,基站(BS)与边缘服务器一起为各种设备、停放的汽车和车流提供服务。研究提出了一种使用基于深度确定性策略梯度(DDPG)的联合学习方法来优化资源分配和作业卸载的合作方法。该方法可根据特定要求将设备操作从设备转移到 BS,或从 BS 转移到车辆。提议的系统还考虑了车辆在离开前在 BS 范围内提供作业卸载服务的持续时间。DDPG-FL 方法的目标是最大限度地减少整体优先级加权任务计算时间。通过模拟结果以及与其他三种方案的比较,该研究证明了他们提出的方法在七种不同场景中的优越性。研究结果凸显了将车辆资源纳入 MEC 系统的潜力,展示了任务处理效率和整体系统性能的提高。
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Resource Allocation Using Deep Deterministic Policy Gradient-Based Federated Learning for Multi-Access Edge Computing

The study focuses on utilizing the computational resources present in vehicles to enhance the performance of multi-access edge computing (MEC) systems. While vehicles are typically equipped with computational services for vehicle-centric Internet of Vehicles (IoV) applications, their resources can also be leveraged to reduce the workload on edge servers and improve task processing speed in MEC scenarios. Previous research efforts have overlooked the potential resource utilization of passing vehicles, which can be a valuable addition to MEC systems alongside parked cars. This study introduces an assisted MEC scenario where a base station (BS) with an edge server serves various devices, parked cars, and vehicular traffic. A cooperative approach using the Deep Deterministic Policy Gradient (DDPG) based Federated Learning method is proposed to optimize resource allocation and job offloading. This method enables the transfer of device operations from devices to the BS or from the BS to vehicles based on specific requirements. The proposed system also considers the duration for which a vehicle can provide job offloading services within the range of the BS before leaving. The objective of the DDPG-FL method is to minimize the overall priority-weighted task computation time. Through simulation results and a comparison with three other schemes, the study demonstrates the superiority of their proposed method in seven different scenarios. The findings highlight the potential of incorporating vehicular resources in MEC systems, showcasing improved task processing efficiency and overall system performance.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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