# When Machine Learning Meets Congestion Control: A Survey and Comparison

arXiv: https://arxiv.org/abs/2010.11397 · HTML: https://mlsanity.com/paper/2010.11397/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Impact of RoCE Congestion Control Policies on Distributed Training of DNNs](https://mlsanity.com/paper/2207.10898/) (2022)
- [Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs](https://mlsanity.com/paper/2207.02295/) (2022)
- [Multimodal and Crossmodal AI for Smart Data Analysis](https://mlsanity.com/paper/2209.01308/) (2022)
- [MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks](https://mlsanity.com/paper/2302.01301/) (2023)
- [Machine Learning Empowered Intelligent Data Center Networking: A Survey](https://mlsanity.com/paper/2202.13549/) (2022)
- [A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks](https://mlsanity.com/paper/2402.04515/) (2024)
- [Specification Generation for Neural Networks in Systems](https://mlsanity.com/paper/2412.03028/) (2024)

## Abstract (arXiv)

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