스마트미디어저널 (Smart Media Journal)

  • 제12권11호
  • /
  • Pages.113-124
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  • 2023
  • /
  • 2287-1322(pISSN)
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  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
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5G 및 B5G 네트워크에서 그래프 신경망 및 강화학습 기반 최적의 VNE 기법

Graph Neural Network and Reinforcement Learning based Optimal VNE Method in 5G and B5G Networks

  • 박석우 (군산대학교 전자정보공학부) ;
  • 문강현 (군산대학교 전자정보공학부) ;
  • 정경택 (군산대학교 전자공학과) ;
  • 나인호 (군산대학교 소프트웨어학부)
  • 투고 : 2023.12.20
  • 심사 : 2023.12.26
  • 발행 : 2023.12.29
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초록

5G 및 B5G(Beyond 5G) 네트워크의 등장으로 기존 네트워크 한계를 극복할 수 있는 네트워크 가상화 기술이 주목받고 있다. 네트워크 가상화의 목적은 효율적 네트워크 자원의 활용과 다양한 전송요구 서비스에 대한 솔루션을 제공하기 위함이다. 이와 관련하여 여러 가지 휴리스틱 기반의 VNE 기법이 연구되고 있으나 네트워크 자원할당 및 서비스의 유연성이 제한되는 문제점을 지니고 있다. 본 논문에서는 다양한 응용의 서비스 요구사항을 충족하기 위해 GNN 기반의 네트워크 슬라이싱 분류 기법과 최적의 자원할당을 위한 RL 기반 VNE 기법을 제안한다. 제안된 기법에서는 Actor-Critic 네트워크를 이용하여 최적의 VNE를 수행한다. 또한 성능 평가를 위해 제안된 기법과 기존의 Node Rank, MCST-VNE, GCN-VNE 기법과의 성능을 비교분석하고 서비스 수용률 제고 및 효율적 자원 할당 측면에서 성능이 향상됨을 보인다.

With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.

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과제정보

이 논문은 2023년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원과 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 염구임(P0012769, 2023년 산업혁신인재성장지원사업, No. 2021R1A2C2014333)

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