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

TL;DR

TelApart is an unsupervised machine learning system that uses telemetry data to automatically differentiate between internal cable network faults and customer-premise faults, aiding efficient maintenance.

Contribution

The paper introduces TelApart, a novel fault diagnosis system utilizing telemetry data and unsupervised learning to distinguish fault types without manual hyper-parameter tuning.

Findings

Effective fault differentiation demonstrated on real-world data

Addresses data quality issues like missing and duplicated data

Hyper-parameter tuning guided by customer trouble tickets

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.