Modeling TCP performance using graph neural networks
Benedikt Jaeger, Max Helm, Lars Schwegmann, Georg Carle · 2022
TCP throughput and RTT prediction are essential to model TCP behavior and optimize network configurations. Flows adapt their sending rate to network parameters like link capacity or buffer size and interact with parallel flows. Especially the elastic behavior of TCP congestion control can vary, even when only slight changes in the network occur. Thus, existing analytical models for TCP behavior reach their limits due to the number and complexity of different algorithms. Machine learning approaches, in contrast, are often fixed to specific network topologies.