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

Balint Bicski, Adrian Pekar

TL;DR

The paper addresses the problem of detecting service degradation in residential networks with hardware-offloaded edge devices. It proposes an intra-flow approach that uses early O-segment features, notably Packet Inter-Arrival Time (PIAT), to infer SD in the non-observable NO segments. A key result is identifying an optimal O/NO split threshold of $\theta=10$, enabling accurate prediction with XGBoost achieving a F1-score of about $0.74$, balanced accuracy of around $0.84$, and AUROC of approximately $0.97$. The work demonstrates the practicality of edge-friendly SD monitoring and provides a basis for robust, proactive network maintenance 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.

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

TL;DR

The paper addresses the problem of detecting service degradation in residential networks with hardware-offloaded edge devices. It proposes an intra-flow approach that uses early O-segment features, notably Packet Inter-Arrival Time (PIAT), to infer SD in the non-observable NO segments. A key result is identifying an optimal O/NO split threshold of , enabling accurate prediction with XGBoost achieving a F1-score of about , balanced accuracy of around , and AUROC of approximately . The work demonstrates the practicality of edge-friendly SD monitoring and provides a basis for robust, proactive network maintenance 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.
Paper Structure (20 sections, 2 equations, 7 figures, 1 table)

This paper contains 20 sections, 2 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Vertical Separation of Delays
  4. Horizontal Separation of Flows
  5. Refining Observability of LAN Delays
  6. Heuristic Detection of Service Degradation Events
  7. Handling Split SD Events
  8. Intra-Flow Service Degradation Detection
  9. Preliminary Evaluation and Performance Analysis
  10. Dataset
  11. Data Preparation
  12. Experimental Design and Evaluation Metrics
  13. Classification Performance
  14. Overall Performance
  15. Model Comparison
  16. ...and 5 more sections

Figures (7)

  • Figure 1: Visual representation of network traffic flow showing the vertical separation between WAN and LAN delays.
  • Figure 2: Illustration of the horizontal separation of a network flow into O and NO segments.
  • Figure 3: An SD event in a flow with a MSL of 2.
  • Figure 4: Illustration of a split SD event in a flow.
  • Figure 5: Illustration of a potential split SD event.
  • ...and 2 more figures