A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP

Abstract

The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences significantly the dynamics and performance of TCP, which is by far the most used communication protocol. Thus, in communication networks, RTT is an important network performance variable. By measuring the traffic at an intermediate node, a network operator or service provider can estimate the RTT and use the estimation to study and troubleshoot the per-connection characteristics and performance. This paper aims at improving the accuracy and timeliness of the RTT estimation, to help network operators improving their analysis. We propose and evaluate a novel deep learning-based model capable of dynamically predicting at real-time the RTT between the sender and receiver with high accuracy based on passive measurements collected at an intermediate node, taking advantage of the commonly used TCP timestamps. We validate extensively our prediction methodology in a controlled experimental testbed and in a realistic scenario on the Google Cloud platform. We show that our model, which is based on classical deep learning algorithms, gives reasonably effective state-of-the-art performance results across multiple TCP congestion control variants. We also show that the model works well for transfer learning. Even though the RTT prediction model was trained on an emulated network, it performs well also when applied to a realistic scenario setting, as demonstrated in our experimental evaluation.