Resource Allocation Method for Network Slicing Using Constrained Reinforcement Learning

Abstract

With the proliferation of mobile networks, we face strong diversification of services, demanding the network to be more flexible. To satisfy this dire need, network slicing is embraced as a promising solution for resource utilization in 5G and future networks. However, this process is complicated that the traditional approaches cannot effectively perform resource orchestration due to the lack of accurate models and the existence of dynamic hidden structures. We formulate the resource allocation problem as a Constrained Markov Decision Process and solve it using constrained reinforcement learning. Specifically, we use the adaptive interior-point policy optimization and projection layers to handle cumulative and instantaneous constraints. Our evaluations show that our method is effective in resource allocation and outperforms baselines.