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...