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The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Machine Learning-based Optimal VNF Deployment Prediction

기계학습 기반 VNF 최적 배치 예측 기술연구

  • Park, Suhyun (Pohang University of Science and Technology Department of Computer Science Engineering) ;
  • Kim, Hee-Gon (Pohang University of Science and Technology Department of Computer Science Engineering) ;
  • Hong, Jibum (Pohang University of Science and Technology Department of Computer Science Engineering) ;
  • Yoo, Jae-Hyung (Pohang University of Science and Technology Department of Computer Science Engineering) ;
  • Hong, James Won-Ki (Pohang University of Science and Technology Department of Computer Science Engineering)
  • Received : 2020.07.17
  • Accepted : 2020.08.16
  • Published : 2020.08.31
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Abstract

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

NFV (Network Function Virtualization) 환경에서는 소프트웨어로 구현된 가상 네트워크 기능 (VNF: Virtualized Network Function)을 범용 서버에 설치하는 것으로 네트워크 기능을 제공한다. 네트워크 관리자는 VNF를 네트워크 토폴로지 상 적절한 위치의 서버에 배치하고 상황에 따라 동적으로 관리함으로써, 다양한 네트워크 상황에 대해 신속하고 유연하게 대응할 수 있다. 하지만 여러 네트워크 조건 (서비스 비용 및 품질) 등을 고려하는 것은 매우 복잡하고 어려운 문제이며, 특히 결정된 배치를 실제 NFV 환경에 적용하는 데는 처리 시간이 소요되기 때문에, 최적의 VNF 배치를 위해서는 필요한 자원량을 예측하여 VNF 배치를 결정하는 것이 필요하다. 본 논문에서는 MEC (Multi-access Edge Computing) 토폴로지에서 서비스 요청을 무작위로 생성하여 ILP (Integer Linear Programming) 모델을 통해 시뮬레이션한 결과를 학습데이터로 사용하는 기계학습 모델을 도출한다. 도출된 예측 모델은 5분 이후의 미래 시점에 대해 ILP 솔루션 결과 대비 90% 이상의 정확도를 보였다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기술진흥센터의 정보통신·방송 연구개발사업의 일환으로 수행하였음.(No. 2018-0-00749, 인공지능 기반 가상 네트워크 관리기술 개발) 본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학 ICT 연구센터지원사업의 연구결과로 수행되었음 (IITP-2020-2017-0-01633)

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