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TelApart: Differentiating Network Faults from Customer-Premise Faults in Cable Broadband Networks

Jiyao Hu, Zhenyu Zhou, Xiaowei Yang

TL;DR

TelApart addresses the practical problem of differentiating maintenance- from service-issues in cable broadband networks using unlabeled PNM telemetry. It decouples fault detection from fault diagnosis, applying unsupervised clustering to identify device groups with similar anomalous patterns and then uses a cluster-size threshold to label maintenance versus service faults. Hyper-parameters are automatically tuned with guidance from customer-ticket statistics, enabling deployment without manual tuning. The approach robustly handles missing, duplicated, and misaligned PNM data and demonstrates strong performance (Rand Index ≈ 0.91) and field-validated effectiveness, offering significant potential to reduce misdispatches and operation costs in real-world ISP settings. The work contributes a turn-key, data-preprocessing–aware fault-diagnosis framework that relies on time-series similarity and ticket-guided optimization rather than labeled training data.

Abstract

Two types of radio frequency (RF) impairments frequently occur in a cable broadband network: impairments that occur inside a cable network and impairments occur at the edge of the broadband network, i.e., in a subscriber's premise. Differentiating these two types of faults is important, as different faults require different types of technical personnel to repair them. Presently, the cable industry lacks publicly available tools to automatically diagnose the type of fault. In this work, we present TelApart, a fault diagnosis system for cable broadband networks. TelApart uses telemetry data collected by the Proactive Network Maintenance (PNM) infrastructure in cable networks to effectively differentiate the type of fault. Integral to TelApart's design is an unsupervised machine learning model that groups cable devices sharing similar anomalous patterns together. We use metrics derived from an ISP's customer trouble tickets to programmatically tune the model's hyper-parameters so that an ISP can deploy TelApart in various conditions without hand-tuning its hyper-parameters. We also address the data challenge that the telemetry data collected by the PNM system contain numerous missing, duplicated, and unaligned data points. Using real-world data contributed by a cable ISP, we show that TelApart can effectively identify different types of faults.

TelApart: Differentiating Network Faults from Customer-Premise Faults in Cable Broadband Networks

TL;DR

TelApart addresses the practical problem of differentiating maintenance- from service-issues in cable broadband networks using unlabeled PNM telemetry. It decouples fault detection from fault diagnosis, applying unsupervised clustering to identify device groups with similar anomalous patterns and then uses a cluster-size threshold to label maintenance versus service faults. Hyper-parameters are automatically tuned with guidance from customer-ticket statistics, enabling deployment without manual tuning. The approach robustly handles missing, duplicated, and misaligned PNM data and demonstrates strong performance (Rand Index ≈ 0.91) and field-validated effectiveness, offering significant potential to reduce misdispatches and operation costs in real-world ISP settings. The work contributes a turn-key, data-preprocessing–aware fault-diagnosis framework that relies on time-series similarity and ticket-guided optimization rather than labeled training data.

Abstract

Two types of radio frequency (RF) impairments frequently occur in a cable broadband network: impairments that occur inside a cable network and impairments occur at the edge of the broadband network, i.e., in a subscriber's premise. Differentiating these two types of faults is important, as different faults require different types of technical personnel to repair them. Presently, the cable industry lacks publicly available tools to automatically diagnose the type of fault. In this work, we present TelApart, a fault diagnosis system for cable broadband networks. TelApart uses telemetry data collected by the Proactive Network Maintenance (PNM) infrastructure in cable networks to effectively differentiate the type of fault. Integral to TelApart's design is an unsupervised machine learning model that groups cable devices sharing similar anomalous patterns together. We use metrics derived from an ISP's customer trouble tickets to programmatically tune the model's hyper-parameters so that an ISP can deploy TelApart in various conditions without hand-tuning its hyper-parameters. We also address the data challenge that the telemetry data collected by the PNM system contain numerous missing, duplicated, and unaligned data points. Using real-world data contributed by a cable ISP, we show that TelApart can effectively identify different types of faults.

Paper Structure

This paper contains 42 sections, 2 theorems, 9 equations, 13 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Hybrid Fiber-Coaxial (HFC) Architecture
  4. Datasets
  5. PNM data
  6. Customer tickets
  7. Ethical Considerations
  8. Related Work
  9. PNM Best Practice
  10. CableMon
  11. Other Related Work
  12. Design Rationale
  13. Challenges
  14. Goals
  15. No manual data labeling:
  16. ...and 27 more sections

Key Result

Theorem 1

Given two time series data $T_x = \{ t_{x1}, t_{x2}, ..., t_{xn} \}$ and $T_y = \{ t_{y1}, t_{y2}, ..., t_{ym} \}$, Algorithm alg:align returns an alignment $A_{(x, y)} = \{(t_{xi}, t_{yj})\}$. Then $\forall\tilde{A}_{(x, y)} = \{(t_{\tilde{xi}}, t_{\tilde{yj}})\}$ such that $|\tilde{A}_{(x, y)}| \g

Figures (13)

  • Figure 1: An overview of the Hybrid Fiber Coaxial (HFC) architecture.
  • Figure 2: Figure (a) shows how the transmission powers of several cable devices in the same fiber optical node fluctuate over time. Orange dots are devices that show the same anomalous transmission power patterns. Green triangles are devices that show normal patterns. Red squares are devices that show distinct anomalous patterns. Figure (b) shows the locations of the cable devices using the same colored icons.
  • Figure 3: An overview of TelApart's design. $C_{thr}$ is a threshold specified by a network operator based on its network topology.
  • Figure 4: This figure shows the probability density function of data collection intervals.
  • Figure 5: This figure shows an example that without inferring missed data points, a naive alignment algorithm may pair up two data points $x_4$ and $y_4$, which are not close in time.
  • ...and 8 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Theorem 2
  • proof