When machine learning meets congestion control: A survey and comparison

Abstract Machine learning has seen a significant surge and uptake across many diverse applications. The high flexibility, adaptability, and computing capabilities it provides extend traditional approaches used in multiple fields including network operation and management. Numerous surveys have explored machine learning algorithms in the context of networking, such as traffic engineering, performance optimization, and network security. Many machine learning approaches focus on clustering, classification, regression, and reinforcement learning. The innovation of this research, and the contribution of this paper lies in the detailed summary and comparison of learning-based congestion control approaches. Compared with traditional congestion control algorithms which are typically rule-based, capabilities to learn from historical experience are highly desirable. From the literature, it is observed that reinforcement learning is a crucial trend among learning-based congestion control algorithms. In this paper, we explore the performance of reinforcement learning-based congestion control algorithms and present current problems with reinforcement learning-based congestion control algorithms. Moreover, we outline challenges and trends related to learning-based congestion control algorithms.