Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance

Author:

Ros Seyha1ORCID,Tam Prohim1ORCID,Song Inseok1,Kang Seungwoo1,Kim Seokhoon12

Affiliation:

1. Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea

2. Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Republic of Korea

Abstract

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

Funder

Institute of Information & communications Technology Planning & Evaluation

Publisher

MDPI AG

Reference44 articles.

1. A Survey of 5G Network: Architecture and Emerging Technologies;Jha;IEEE Access,2015

2. A Survey on beyond 5G Network with the Advent of 6G: Architecture and Emerging Technologies;Dogra;IEEE Access,2020

3. A Review of IoT Network Management: Current Status and Perspectives;Aboubakar;J. King Saud Univ. Comput. Inf. Sci.,2021

4. Network Function Virtualization: State-of-The-Art and Research Challenges;Mijumbi;IEEE Commun. Surv. Tutor.,2016

5. Virtual Network Function Placement for Serving Weighted Services in NFV-Enabled Networks;Dung;IEEE Syst. J.,2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3