Congestion or No Congestion: Packet Loss Identification and Prediction Using Machine Learning

TL;DR

This paper proposes machine learning methods to accurately identify whether packet losses are due to congestion or wireless link issues, aiming to improve TCP performance in hybrid networks.

Contribution

It introduces a machine learning approach using features available at end hosts to differentiate between congestive and non-congestive packet losses, enhancing network management.

Findings

Random Forest and KNN classifiers outperform other models in prediction accuracy.

The approach effectively distinguishes packet loss types using simple host-based features.

Results suggest potential for improving TCP throughput in hybrid networks.

Abstract

Packet losses in the network significantly impact network performance. Most TCP variants reduce the transmission rate when detecting packet losses, assuming network congestion, resulting in lower throughput and affecting bandwidth-intensive applications like immersive applications. However, not all packet losses are due to congestion; some occur due to wireless link issues, which we refer to as non-congestive packet losses. In today's hybrid Internet, packets of a single flow may traverse wired and wireless segments of a network to reach their destination. TCP should not react to non-congestive packet losses the same way as it does to congestive losses. However, TCP currently can not differentiate between these types of packet losses and lowers its transmission rate irrespective of packet loss type, resulting in lower throughput for wireless clients. To address this challenge, we use machine learning techniques to distinguish between these types of packet losses at end hosts, utilizing easily available features at the host. Our results demonstrate that Random Forest and K-Nearest Neighbor classifiers perform better in predicting the type of packet loss, offering a promising solution to enhance network performance.