Detection of AQM on Paths using Machine Learning Methods

TL;DR

This paper presents a machine learning-based method to accurately identify whether a network bottleneck router uses AQM or drop-tail schemes using RTT and CWND data from a single flow.

Contribution

It introduces a novel classification algorithm that relies solely on RTT and CWND metrics to distinguish between AQM and drop-tail queuing schemes.

Findings

High classification accuracy across diverse network topologies.

Effective use of RTT and CWND data for scheme detection.

Robust performance in complex network scenarios.

Abstract

In this paper, we address the problem of determining whether a bottleneck router on a given network path is using an AQM or a drop-tail scheme. We assume that we are given a source-to-sink path of interest -along which a bottleneck router exists- and data regarding the Round-Trip Times (RTT) and Congestion Window (CWND) sizes with respect to this flow. We develop a reliable classification algorithm that solely uses RTT and CWND information pertaining to a single flow to classify the queuing scheme, Tail Drop or AQM, used by the bottleneck router. We evaluate our method and present results that demonstrate our algorithm's highly accurate classification ability across a wide array of complex network topologies and configurations.