An Artificial Intelligence Framework for Slice Deployment and Orchestration in 5G Networks

• Published in: IEEE transactions on cognitive communications and networking Vol. 6; no. 2; pp. 858 - 871
• Main Authors: Dandachi, Ghina, De Domenico, Antonio, Hoang, Dinh Thai, Niyato, Dusit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; admission and congestion control; Admission control; Algorithms; Artificial intelligence; Cloud computing; Computer architecture; Computer simulation; Decisions; Feature extraction; Network slicing; Noise measurement; Priorities; reinforcement learning; Resource management; Resource utilization; Wireless networks
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2019.2952882

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.