Reinforcement Learning based Multi-Attribute Slice Admission Control for Next-Generation Networks in a Dynamic Pricing Environment

• Published in: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) pp. 1 - 5
• Main Authors: Ferreira, Victor C., Esmat, H. H., Lorenzo, Beatriz, Kundu, Sandip, Felipe M. G., Franca
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2022
• Subjects: Admission control; Computational modeling; Heuristic algorithms; Navigation; Network slicing; NextG; Pricing; Process control; Reinforcement learning; slice admission control
• ISSN: 2577-2465
• DOI: 10.1109/VTC2022-Spring54318.2022.9860729

Next-generation networks will provide intelligent infrastructure and management using machine learning. In real-world applications, demand for resources and performance within a service class may vary over time. Infrastructure providers choose which requests to accept with the goal of long-term profit maximization - a process known as slice admission control. In this paper, we envision a dynamic system with varying service requests attributes in urgency, duration, and amount of resources (i.e., computing, network, and storage). Further, we develop a dynamic pricing model that is responsive to demand and supply resulting in demand and supply reserve shaping. Then, we propose a solution to the slice admission control problem by using a reinforcement learning approach with Deep-Q Networks, where the state of the system is modeled using an array of parameters, similar to the input matrix in computer vision. Results show that our computer vision-inspired approach is capable of learning how the better policy to navigate this complex environment by selecting service requests that maximize the provider's long-term profit.