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 language | English |
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Pages (from-to) | 1804-1816 |
Number of pages | 13 |
Journal | IEEE Transactions on Network and Service Management |
Volume | 20 |
Issue number | 2 |
DOIs | |
Publication status | Published - 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