On using reinforcement learning for network slice admission control in 5G: Offline vs. online
Résumé
Achieving a fair usage of network resources is of vital importance in Slice-ready
5G network. The dilemma of which network slice to accept or to reject is very
challenging for the Infrastructure Provider (InfProv). On one hand, InfProv aims to
maximize the network resources usage by accepting as many network slices as possible;
on the other hand, the network resources are limited, and the network slice
requirements regarding Quality of Service (QoS) need to be fulfilled. In this paper,
we devise three admission control mechanisms based on Reinforcement Learning,
namely Q-Learning, Deep Q-Learning, and Regret Matching, which allow deriving
admission control decisions (policy) to be applied by InfProv to admit or reject
network slice requests. We evaluated the three algorithms using computer simulation,
showing results on each mechanism’s performance in terms of maximizing the
InfProv revenue and their ability to learn offline or online.