Fine-Grained Network Traffic Classification with Contextual QoS Profiling
This work addresses the need for precise in-app QoS variations in network traffic classification, which is crucial for managing applications such as edge computing and real-time XR, though it appears incremental as it builds on existing graph neural network approaches.
The paper tackled the problem of fine-grained network traffic classification for applications with strict Quality of Service (QoS) demands, proposing a hierarchical graph neural network framework that significantly outperforms state-of-the-art methods in service-level classification across 14 usage scenarios from platforms like YouTube and Zoom.
Accurate network traffic classification is vital for managing modern applications with strict Quality of Service (QoS) demands, such as edge computing, real-time XR, and autonomous systems. While recent advances in application-level classification show high accuracy, they often miss fine-grained in-app QoS variations critical for service differentiation. This paper proposes a hierarchical graph neural network (GNN) framework that combines a three-level graph representation with an automated QoS-aware assignment algorithm. The model captures multi-scale temporal patterns via packet aggregation, time-window clustering, and session-level behavior modeling. QoS priorities are derived using five key metrics (bandwidth, jitter, packet stability, burst frequency, and burst stability), processed through logarithmic transformation and weighted ranking. Evaluations across 14 usage scenarios from YouTube, Prime Video, TikTok, and Zoom show that the proposed GNN significantly outperforms state-of-the-art methods in service-level classification. The QoS-aware assignment further refines classification to enhance user experience. This work advances QoS-aware traffic classification by enabling precise in-app usage differentiation and adaptive service prioritization in dynamic network environments.