On the Feasibility of Inter-Flow Service Degradation Detection

2025-09-14

AI Summary

Modern network hardware acceleration renders data flows unobservable, creating monitoring blind spots that severely impede real-time service degradation (SD) detection. To address this, we propose treating observable flows as environmental sensors and introduce the first formal framework for inter-flow correlation detection, uncovering the sparsity and precursor nature of SD signals. To overcome the limitation of simple models in capturing complex contextual dependencies across flows, we design a structure-aware cross-flow association analysis method that jointly leverages statistical analysis and machine learning, augmented by feature importance analysis to identify critical correlation patterns. Experimental evaluation demonstrates the feasibility of cross-flow prediction, achieving high classification accuracy. However, we also find that existing models remain overly reliant on intra-flow features, highlighting an urgent need for higher-order contextual modeling. Our work establishes foundational principles for observable-flow–based SD detection and advances the state of the art in inter-flow dependency analysis for production network monitoring.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Hardware acceleration in modern networks creates monitoring blind spots by offloading flows to a non-observable state, hindering real-time service degradation (SD) detection. To address this, we propose and formalize a novel inter-flow correlation framework, built on the hypothesis that observable flows can act as environmental sensors for concurrent, non-observable flows. We conduct a comprehensive statistical analysis of this inter-flow landscape, revealing a fundamental trade-off: while the potential for correlation is vast, the most explicit signals (i.e., co-occurring SD events) are sparse and rarely perfectly align. Critically, however, our analysis shows these signals frequently precede degradation in the target flow, validating the potential for timely detection. We then evaluate the framework using a standard machine learning model. While the model achieves high classification accuracy, a feature-importance analysis reveals it relies primarily on simpler intra-flow features. This key finding demonstrates that harnessing the complex contextual information requires more than simple models. Our work thus provides not only a foundational analysis of the inter-flow problem but also a clear outline for future research into the structure-aware models needed to solve it.

Problem

• Detecting service degradation in non-observable hardware-accelerated flows
• Establishing observable flows as sensors for hidden flow correlation
• Overcoming sparse alignment of degradation signals across concurrent flows

Innovation

• Inter-flow correlation framework for degradation detection
• Statistical analysis revealing predictive signal patterns
• Machine learning evaluation showing need for complex models