  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.
