Automated Deployment of Virtual Network Function in 5G Network Slicing Using Deep Reinforcement Learning

Abstract

Fifth-generation mobile technologies introduce the concept of network slicing, which allows the creation of logical networks consisting of network services and the associated physical and virtual network functions. The early form of network slicing allowed for fixed resource allocation and static network function deployment. However, this approach can lead to inefficiency and service degradation. This study aims to optimize the deployment of virtual network functions within a hybrid cloud infrastructure from the perspective of mission-critical communications. The first task involves designing a deep reinforcement learning-based scheme to determine a significant deployment policy that minimizes the overall delays and costs of logical networks. The scheme performance is evaluated by using a simulated traffic dataset that followed Poisson distributions for a wide range of configurations. In dynamic environments with stationary traffic patterns, simulation results show that the scheme outperforms the one-step look-ahead and fixed-location algorithms by 35.80% and 52.16%, respectively, on average. A value iteration-based scheme is used as a benchmark and only surpasses the proposed scheme by 3.5% on average. Simulation results using a real-world traffic dataset show that the scheme can support nonstationary traffic patterns and cater to large-scale scenarios with many suitable deployment locations by leveraging a function that indicates the relative importance of selecting one location over the others.

Keywords

• 5G mobile communication
• decision support systems
• edge computing
• intelligent agents
• machine learning
• multi-layer neural network