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.

翻译：本研究提出了一种利用早期流特征预测计算机网络中服务性能退化（SD）的新方法。我们的方法聚焦于网络流的可观测（O）段，特别是分析数据包到达间隔时间（PIAT）值及其他衍生指标，以推断不可观测（NO）段的行为。通过全面评估，我们确定了10个观测延迟样本作为最优的O/NO分割阈值，在预测精度与资源利用率之间取得了平衡。在评估包括逻辑回归、XGBoost和多层感知器在内的模型后，我们发现XGBoost表现最佳，其F1分数达到0.74，平衡准确率为0.84，AUROC为0.97。我们的研究结果突显了整合全面早期流特征的有效性，以及本方法为资源受限环境中的网络流量监控提供实用解决方案的潜力。该方法通过预先应对潜在的SD，确保了增强的用户体验和网络性能，为构建维护高质量网络服务的稳健框架奠定了基础。