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

TL;DR

This paper introduces a new intra-flow method for early detection of network service degradation using observable flow features, achieving high prediction accuracy and resource efficiency.

Contribution

The study proposes a novel intra-flow approach focusing on early flow features and identifies optimal parameters for predicting service degradation.

Findings

XGBoost achieves an F1-score of 0.74

Optimal O/NO split threshold is 10 samples

Method effectively predicts SD in resource-constrained environments

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.