# A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches

arXiv: https://arxiv.org/abs/2309.09372 · HTML: https://mlsanity.com/paper/2309.09372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 80 references that are in the map.
- First posted on arXiv 17 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [Learning to Harness Bandwidth with Multipath Congestion Control and Scheduling](https://mlsanity.com/paper/2105.14271/) (2021)
- [FALCON: Fast and Accurate Multipath Scheduling using Offline and Online Learning](https://mlsanity.com/paper/2201.08969/) (2022)
- [Forward Delay-based Packet Scheduling Algorithm for Multipath TCP](https://mlsanity.com/paper/1501.03196/) (2015)
- [Exploiting Path Diversity in Datacenters using MPTCP-aware SDN](https://mlsanity.com/paper/1511.09295/) (2015)
- [MPTCP Linux Kernel Congestion Controls](https://mlsanity.com/paper/1812.03210/) (2018)

## Abstract (arXiv)

Multipath TCP (MPTCP) has been widely used as an efficient way for communication in many applications. Data centers, smartphones, and network operators use MPTCP to balance the traffic in a network efficiently. MPTCP is an extension of TCP (Transmission Control Protocol), which provides multiple paths, leading to higher throughput and low latency. Although MPTCP has shown better performance than TCP in many applications, it has its own challenges. The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly. Moreover, communication latency can occur if the packets are not scheduled correctly between the subflows. This paper reviews techniques to solve the above-mentioned problems based on two main approaches; non data-driven (classical) and data-driven (Machine Learning) approaches. This paper compares these two approaches and highlights their strengths and weaknesses with a view to motivating future researchers in this exciting area of machine learning for communications. This paper also provides details on the simulation of MPTCP and its implementations in real environments.