Applications of Reinforcement Learning in Virtual Network Function Placement: A Survey

In recent years, network function virtualization has attracted massive attention in academia and industry,and the virtual network functions placement problem is one of them. Reinforcement learning has been widely applied in network control and decision, which can learn the optimal policy according to the environment feedback automatically. This paper presents a new summary of the virtual network functions placement problem based on reinforcement learning. We will give a detailed description of how to use reinforcement learning to solve virtual network function placement in different scenarios, then the prospect of further research is forecasted preliminarily.

I. Introduction

Network function virtualization (NFV) is one of the core technologies of 5G, which decouples network functions (NFs) from the special hardware to software. We denote the software as virtual network functions (VNFs). Thanks to NFV and software defined network, a conventional telecommunications network becomes a programmable platform, which can be tailored to satisfy different cus-tomers' specific needs.