Mobility-aware Service Migration in MEC System

Multi-access edge computing (MEC) has emerged as an effective approach for enhancing system quality. Nevertheless, the movement of users and variations in demand for the service might lead to an increase in system delays. This article investigates the issue of service migration, with a particular fo...

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Bibliographic Details
Published in2024 International Conference on Information Networking (ICOIN) pp. 653 - 656
Main Authors Tran, Tuan Phong, Yoo, Myungsik
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.01.2024
Subjects
Deep reinforcement learning
Delays
Energy consumption
Markov decision process
Markov decision processes
MEC
Multi-access edge computing
Predictive models
Recurrent neural network
Recurrent neural networks
service migration
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Summary:Multi-access edge computing (MEC) has emerged as an effective approach for enhancing system quality. Nevertheless, the movement of users and variations in demand for the service might lead to an increase in system delays. This article investigates the issue of service migration, with a particular focus on the factors of mobility and service availability. Specifically, we model the Markov Decision Process (MDP) problem. To make effective service migration decisions, we propose a deep reinforcement learning (DRL) model. Furthermore, a recurrent neural network (RNN) is implemented in order to enhance model performance by predicting user movement. The experimental results demonstrate the effectiveness of the proposed method in reducing the system delay.
DOI:10.1109/ICOIN59985.2024.10572184