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.
|Journal||IEEE Transactions on Network and Service Management|
|Publication status||Accepted/In press - 2022|
- 5G mobile communication
- Cloud computing
- Cloud-native network function placement
- deep reinforcement learning
- Non-public network.
- Prediction algorithms
- Radio access networks
- Spatiotemporal phenomena
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering