Intelligent Active Queue Management Using Explicit Congestion Notification

TL;DR

This paper proposes an intelligent AQM method that leverages ECN and machine learning, specifically neural networks and reinforcement learning, to predict congestion and optimize buffer management, improving network performance.

Contribution

It introduces a novel ML-based approach combining neural networks and reinforcement learning to enhance AQM parameter tuning using ECN data.

Findings

Improved congestion prediction accuracy.

Enhanced network throughput and reduced latency.

Effective AQM parameter tuning with ML techniques.

Abstract

As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.