Evaluating TCP Throughput Predictability from Packet Traces using Recurrent Neural Network

Abstract

Congestion control algorithms using recurrent neural network (RNN) for bandwidth prediction are expected to improve throughput. Previous studies involving performance evaluations were conducted only using simulated data. However, simulation and real-world environments are largely different and rarely provide equivalent prediction accuracy. Therefore, we will verify whether our proposed method provides better prediction accuracy in a real-world environment. We measured communications in a real environment and generated training data by converting packet captured data with measurement of prediction accuracy on the generated data. The results showed that the maximum percentage of correct responses was 79.71%, which was comparable to the results obtained using simulated data.