摘要：Multiaccess edge computing (MEC) provides services for resource-sensitive and delay-sensitive Internet of Things (IoT) applications by extending the capabilities of cloud computing to the edge of the networks. However, the high mobility of IoT devices (e.g., vehicles) and the limited resources of edge servers (ESs) affect the service continuity and access latency. Service migration and reasonable resource (re-)allocation consequently become needed to ensure Quality of Service (QoS). However, service migration results in additional latency. In addition, different mobile IoT users have different resource requirements and different resource allocation policies of target ESs also determine whether service migration is necessary. Subsequently, how to jointly optimize service migration and resource allocation (SMRA) is a challenge that needs to be carefully addressed. To this end, this article investigates the joint optimization problem of SMRA in MEC environments to minimize the access delay of IoT users. It proposes a joint SMRA algorithm based on deep reinforcement learning (DRL), which takes into account the mobility of IoT users and decides whether to migrate services, where to migrate, and how to allocate resources through the long short time memory (LSTM) algorithm and the parameterized deep Q -network (PDQN) algorithm. Moreover, the PDQN algorithm effectively solves the discrete-continuous hybrid action space challenge in the SMRA problem. Finally, we conduct evaluation using a real-world data set of Beijing cab trajectories to verify the effectiveness and superiority of our proposed SMRA solution. ? 2014 IEEE.

ISSN号：2327-4662

卷、期、页：卷11期7：11341-11352

发表日期：2024-04-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：IEEE Internet of Things Journal

参与作者：Yu, Hao,Taleb, Tarik

通讯作者：刘芳正

第一作者：黄霁崴

论文类型：期刊论文

论文概要：刘芳正,Yu, Hao,黄霁崴,Taleb, Tarik，Joint Service Migration and Resource Allocation in Edge IoT System Based on Deep Reinforcement Learning，IEEE Internet of Things Journal，2024，卷11期7：11341-11352

论文题目：Joint Service Migration and Resource Allocation in Edge IoT System Based on Deep Reinforcement Learning