A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches

Abstract

This paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-the-art applications in communication networking, which reveal the potential to revolutionize access, transport, and core network management policies. This paper further explores DRL capabilities, its variants, and the trending applications in E2E networking, particularly in enhancing dynamic network (re)configurations and resource management. By fusing GNN with DRL, we spotlight novel approaches, ranging from radio access networks to core management and orchestration, across E2E network layers. Deployment scenarios in smart transportation, smart factory, and smart grids demonstrate the practical implications of our survey topic. Lastly, we point out potential challenges and future research directions, including the critical aspects for modelling explainability, the reduction in overhead consumption, interoperability with existing schemes, and the importance of reproducibility. Our survey aims to serve as a roadmap for future developments in E2E networking, guiding through the current landscape, challenges, and prospective breakthroughs in the algorithm modelling toward network automation using GNN and DRL.