Early Detection of Network Service Degradation: An Intra-Flow Approach
<|reference_start|>Early Detection of Network Service Degradation: An Intra-Flow Approach: 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.<|reference_end|>
@article{bicski2024early,
    title={Early Detection of Network Service Degradation: An Intra-Flow Approach},
    author={Balint Bicski and Adrian Pekar},
    journal={arXiv preprint arXiv:2407.06637},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06637},
    primaryClass={cs.NI cs.LG}
}