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

TL;DR

This survey compares classical and machine learning approaches to congestion control and scheduling in Multipath TCP, highlighting their strengths, weaknesses, and potential for future research in improving network performance.

Contribution

It provides a comprehensive comparison of data-driven and classical methods for MPTCP congestion control and scheduling, and discusses simulation and real-world implementations.

Findings

Machine learning approaches can adapt better to dynamic network conditions.

Classical methods are simpler but less flexible in complex scenarios.

Hybrid approaches show promise for future improvements.

Abstract

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.