Journal
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
Volume 6, Issue 2, Pages 858-871Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2019.2952882
Keywords
Resource management; 5G mobile communication; Admission control; Artificial intelligence; Cloud computing; Noise measurement; Computer architecture; Network slicing; reinforcement learning; admission and congestion control
Categories
Funding
- France-Singapore MERLION 2017-2018
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure [NSoE DeST-SCI2019-0007]
- A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing [RGANS1906, WASP/NTU M4082187 (4080)]
- Singapore MOE [2017-T1-002-007 RG122/17, MOE2014-T2-2-015 ARC4/15, NRF2017EWTEP003-041]
- 5G-MoNArch project [761445]
- European Commission
Ask authors/readers for more resources
Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication, computing, and storage resources to maximize resources utilization and operator revenue. First, we propose a smart feature extraction solution to analyze the characteristics of incoming requests together with the already deployed slices, and then automatically evaluates the request requirements to make appropriate decisions. Second, we design an online algorithm that controls the slice admission based on their priorities, the arrival and departure characteristics, and the available resources. To mitigate system overloading, our framework dynamically adjusts resources allocated to low priority slices, thereby reducing the dropping probability of new slice requests. The proposed algorithm offers outstanding advantages over traditional static approaches by automatically adapting the controller decisions to the system changes. Simulation results show that our framework significantly improves the resource utilization and reduces the slice request dropping probabilities up to 44% as compared to the baseline schemes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available