Fine-Grained Network Traffic Classification with Contextual QoS Profiling

2026-03-17

AI Summary

Existing application-level traffic classification methods struggle to discern fine-grained quality-of-service (QoS) variations within applications, limiting their ability to meet the precise service demands of edge computing and real-time extended reality (XR) scenarios. This work proposes a hierarchical graph neural network framework that, for the first time, integrates QoS context into fine-grained traffic classification. The approach constructs a three-tier graph structure through packet aggregation, time-window clustering, and session behavior modeling, and incorporates a multi-scale QoS-aware weighting and ranking mechanism based on bandwidth, jitter, packet stability, burst frequency, and consistency to enable automatic priority assignment. Evaluated across 14 real-world applications—including YouTube and Zoom—the method significantly improves service-level classification accuracy and effectively enhances user experience.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Fine-Grained Traffic Classification
• Quality of Service (QoS)
• In-App QoS Variation
• Service Differentiation
• Network Traffic Management

Innovation

• Graph Neural Network
• QoS-aware classification
• fine-grained traffic analysis
• multi-scale temporal modeling
• service prioritization