Deep Reinforcement Learning based Cloud-native Network Function Placement in Private 5G Networks

Joonwoo Kim, Jaewook Lee, Taeyun Kim, Sangheon Pack

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the advantages of satisfying service requirements and providing high security, standalone private fifth generation (5G) network is perceived as a promising technology for vertical industries. However, to manage the cloud-native network functions (CNFs) in an effective manner, a sophisticated control plane management scheme should be designed in standalone private 5G networks. In this paper, we propose a deep Q-network based CNF placement algorithm (DQN-CNFPA), that jointly minimizes the cost occurred in launching and operating CNFs on edge clouds and the back-haul control traffic overhead. In addition, DQN-CNFPA learns spatiotemporal patterns in service requests and places CNFs in consideration of future cost leveraged by the previous CNF placement strategy. Evaluation results demonstrate that DQN-CNFPA can reduce the cost per hour up to 11.2% compared to the scheme without learning spatiotemporal service request patterns.

Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
Publication statusPublished - 2020 Dec
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 2020 Dec 72020 Dec 11

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
CountryTaiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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