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

8 Citations (Scopus)

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
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This work was supported in part by Samsung Research in Samsung Electronics and in part by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIP) (No. 2020R1A2C3006786).

Publisher Copyright:
© 2020 IEEE.

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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