A Survey on Congestion Control and Scheduling for Multipath TCP: Machine
  Learning vs Classical Approaches
Maisha Maliha Golnaz Habibi Mohammed Atiquzzaman
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.