Deep Reinforcement Learning based Mobility-Aware Service Migration for Multi-access Edge Computing Environment

Yaqiang Zhang, Rengang Li, Yaqian Zhao, Ruyang Li
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引用次数: 2

Abstract

Multi-access Edge Computing (MEC) plays an im-portant role for providing end users with high reliability and low latency services at the edge of mobile network. In the scenario of Internet of Vehicles (IoV), vehicle users continually access nearby base stations to offload real-time tasks for reducing their computing overhead, while the ongoing services on current deployed edge nodes may be far away from users with the vehicles moving, potentially resulting in a high delay of data transmission. To address this challenge, in this paper, we propose a Deep Reinforcement Learning (DRL)-based mobility-aware service migration mechanism for effectively reducing the service delay and migration delay of the network. The proposed technique is adopted by re-calibrating required services at edge locations near the mobile user. Edge network state and user movement information are considered to ensure the generation of real-time service migration decision. Extensive experiments are conducted, and evaluation results demonstrate that our proposed DRL-based technique can effectively reduce the long-term average delay of the MEC system, compared with the state-of-the-art techniques.
基于深度强化学习的多访问边缘计算环境下移动感知业务迁移
多接入边缘计算(Multi-access Edge Computing, MEC)在为终端用户提供高可靠性、低时延的移动网络边缘服务方面发挥着重要作用。在车联网场景下,车辆用户不断访问附近的基站,以卸载实时任务,以减少其计算开销,而当前部署的边缘节点上正在进行的业务可能随着车辆的移动而远离用户,可能导致数据传输的高延迟。为了解决这一挑战,本文提出了一种基于深度强化学习(DRL)的移动感知服务迁移机制,以有效降低网络的服务延迟和迁移延迟。该技术通过在移动用户附近的边缘位置重新校准所需的服务来实现。考虑边缘网络状态和用户移动信息,确保实时业务迁移决策的生成。实验结果表明,与现有技术相比,我们提出的基于drl的技术可以有效地降低MEC系统的长期平均延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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