Deep Q-Network-based Cloud-Native Network Function Placement in Edge Cloud-Enabled Non-Public Networks

Joonwoo Kim, Jaewook Lee, Taeyun Kim, Sangheon Pack

Research output: Contribution to journalArticlepeer-review

Abstract

Owing to the advantages of satisfying service requirements and providing strong security, non-public networks (NPNs) are considered as a promising technology in vertical industries. However, to efficiently manage cloud-native network functions (CNFs) in NPNs, a sophisticated control plane management scheme should be designed. In this paper, we propose a deep Q-network-based CNF placement algorithm (DQN-CNFPA) that jointly minimizes the costs incurred by launching and operating CNFs in edge clouds and the backhaul control traffic overhead. In addition, DQN-CNFPA learns the spatiotemporal patterns in service requests and adaptively places CNFs in edge clouds according to the expected incurred costs. The evaluation results demonstrate that DQN-CNFPA can reduce the total cost by up to 26.2% compared with a conventional scheme that does not learn spatiotemporal service request patterns.

Original languageEnglish
JournalIEEE Transactions on Network and Service Management
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • 5G mobile communication
  • Cloud computing
  • Cloud-native network function placement
  • Costs
  • deep reinforcement learning
  • Non-public network.
  • Optimization
  • Prediction algorithms
  • Radio access networks
  • Spatiotemporal phenomena

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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