Exploring Machine Learning for Classification of QUIC Flows over Satellite

TL;DR

This paper presents a machine learning-based architecture for classifying QUIC traffic over satellite networks to support QoS, leveraging traffic profiles from packet sizes and timings without explicit path setup.

Contribution

It introduces a novel ML-driven traffic classification method for hybrid terrestrial and satellite networks that enables soft QoS without explicit path configuration.

Findings

ML models effectively classify QUIC flows over satellite links

The approach supports adaptive QoS in hybrid networks

Higher computational power facilitates deployment of ML in SATCOM equipment

Abstract

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevents intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require an explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at a low cost creates fertile ground for the implementation of these techniques.