Early Detection of Network Service Degradation: An Intra-Flow Approach

• URL: http://arxiv.org/abs/2407.06637v2
• Date: Sun, 15 Sep 2024 19:07:30 GMT
• Title: Early Detection of Network Service Degradation: An Intra-Flow Approach
• Authors: Balint Bicski, Adrian Pekar,
• Abstract summary: This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet Inter-Arrival Time (PIAT) We identify an optimal O/NO split threshold of 10 observed delay samples, balancing prediction accuracy and resource utilization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet Inter-Arrival Time (PIAT) values and other derived metrics, to infer the behavior of non-observable (NO) segments. Through a comprehensive evaluation, we identify an optimal O/NO split threshold of 10 observed delay samples, balancing prediction accuracy and resource utilization. Evaluating models including Logistic Regression, XGBoost, and Multi-Layer Perceptron, we find XGBoost outperforms others, achieving an F1-score of 0.74, balanced accuracy of 0.84, and AUROC of 0.97. Our findings highlight the effectiveness of incorporating comprehensive early flow features and the potential of our method to offer a practical solution for monitoring network traffic in resource-constrained environments. This approach ensures enhanced user experience and network performance by preemptively addressing potential SD, providing the basis for a robust framework for maintaining high-quality network services.

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