  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.
