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

5 Citations (Scopus)

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
Pages (from-to)1804-1816
Number of pages13
JournalIEEE Transactions on Network and Service Management
Volume20
Issue number2
DOIs
Publication statusPublished - 2023 Jun 1

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

  • Cloud-native network function placement
  • deep reinforcement learning
  • non-public network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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