Machine Learning Applications in the Routing in Computer Networks
This is an incremental survey that addresses the problem of improving routing performance and scalability for network engineers and researchers.
This survey examines how machine learning techniques can enhance routing algorithms for internet traffic, finding that many approaches show promise in optimizing network routing but often rely on simulations with potentially unrealistic configurations.
Development of routing algorithms is of clear importance as the volume of Internet traffic continues to increase. In this survey, there is much research into how Machine Learning techniques can be employed to improve the performance and scalability of routing algorithms. We surveyed both centralized and decentralized ML routing architectures and using a variety of ML techniques broadly divided into supervised learning and reinforcement learning. Many of the papers showed promise in their ability to optimize some aspect of network routing. We also implemented two routing protocols within 14 surveyed routing algorithms and verified the efficacy of their results. While the results of most of the papers showed promise, many of them are based on simulations of potentially unrealistic network configurations. To provide further efficacy to the results, more real-world results are necessary.