Machine Learning for End-to-End Congestion Control

End-to-end congestion control has been extensively studied for over 30 years as one of the most important mechanisms to ensure efficient and fair sharing of network resources among users. As future networks are becoming more and more complex, conventional rule-based congestion control approaches tend to become inefficient and even ineffective. Inspired by the great success that machine learning (ML) has achieved in addressing large-scale and complex problems, researchers have begun to shift their attention from the rule-based method to an ML-based approach. This article presents a selected review of the recent applications of ML to the field of end-to-end congestion control. In this survey, we start with a brief review of the relationship between congestion control and ML. We then review the recent works that apply ML to congestion control. These works either help the agent to make an intelligent congestion control decision or achieve enhanced performance. Finally, we highlight a series of realistic challenges and shed light on potential future research directions.