Review on Reinforcement Learning-based approaches for Service Function Chain deployment in 5G networks

Abstract

5G networks are capable of supporting a wide range of applications with different requirements, which brings several use cases for mobile networks and increases user demands. The advancement of 5G is dependent on new technologies such as Software Defined Networks (SDN), Network Function Virtualization (NFV), and Service Function Chain (SFC). SDN enables the separation of control and data planes. NFV decouples network functions from hardware using virtualization. SFC is a popular service paradigm that has been proposed to derive maximum benefits from both NFV and SDN in 5G networks. The infrastructure of 5G networks brings a change in the network management approaches for deploying network services by allocating resources and determining optimal forwarding paths. The existing deployment methods have some shortcomings that require complete knowledge of the system. For that, Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL), which have demonstrated success in solving complex control and decision-making problems by allowing network entities to learn, build knowledge, and make optimal decisions separately, are used to deploy network services dynamically, which has inspired many researchers to start developing new techniques by combining machine learning approaches to solve specific networking problems. This paper reviews RL and DRL techniques that have been studied and implemented in order to deploy SFC in 5G infrastructure networks, by providing a basic description of concepts and a clear problems explication that helps new searchers invest their effort in implementing new approaches and improving existing ones.