A Reinforcement Learning-Based Admission Control Strategy for Elastic Network Slices
- Resource Type
- Conference
- Authors
- Wu, Zhouxiang; Ishigaki, Genya; Gour, Riti; Jue, Jason P.
- Source
- 2021 IEEE Global Communications Conference (GLOBECOM) Global Communications Conference, (GLOBECOM) 2021 IEEE. :01-06 Dec, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Training
Recurrent neural networks
Q-learning
Conferences
Admission control
Training data
Channel allocation
Network Slicing
Admission Control
Reinforce-ment Learning
Proximal Policy Optimization
- Language
This paper addresses the problem of admission control for elastic network slices that may dynamically adjust provisioned bandwidth levels over time. When admitting new slice requests, sufficient spare capacity must be reserved to allow existing elastic slices to dynamically increase their bandwidth allocation when needed. We demonstrate a lightweight deep Reinforcement Learning (RL) model to intelligently make ad-mission control decisions for elastic slice requests and inelastic slice requests. This model achieves higher revenue and higher acceptance rates compared to traditional heuristic methods. Due to the lightness of this model, it can be deployed without GPUs. We can also use a relatively small amount of data to train the model and to achieve stable performance. Also, we introduce a Recurrent Neural Network to encode the variable-size environment and train the encoder with the RL model together.