Improvement of inter-protocol fairness for BBR congestion control using machine learning

Google's BBR (Bottleneck Bandwidth and Round-trip Propagation Time) approach is used to enhance internet network transmission. It is particularly intended to efficiently handle enormous amounts of data. Traditional TCP (Transmission Control Protocol) algorithms confront the most difficulty in calculating the proper quantity of data to send in order to prevent congestion and bottlenecks. This wastes bandwidth and causes network delays. BBR addresses this issue by adaptively assessing the available bandwidth (also known as bottleneck bandwidth) along the network channel and calculating the round-trip time (RTT) between the sender and receiver. Although when several flows compete for bandwidth, BBR may supply more bandwidth to one flow at the expense of another, resulting in unequal resource distribution. This paper proposes to integrate machine learning with BBR to enhance fairness in resource allocation. This novel strategy can improve bandwidth allocation and provide a more equal distribution of resources among competing flows by using historical BBR data to train an ML model. Further we also implemented a classifier model that is graphic neural network in the congestion control method.