Characterizing and Detecting Physical Layer Issues in Cable Broadband Networks

Cable broadband networks are one of the few ``last-mile'' broadband technologies widely available in the U.S. The COVID-19 pandemic has highlighted the critical role that broadband networks play in the US economy and society. Unfortunately, Cable broadband networks usually suffer from poor reliability. Many cable broadband networks in the United States were built in the 1990s and early 2000s, which result in poor application-layer performance, e.g., high packet loss rate, slow web responses, or low-quality video streaming. Improving the reliability of cable broadband networks satisfies both customer experiences and operator needs.

This work aims to improve the reliability of cable broadband networks with the help of a regional cable ISP. Our cooperating ISP provided telemetry data collected by the Proactive Network Maintenance (PNM) infrastructure in cable networks. The data is collected from 77K+ cable modems that spread across 394 hybrid-fiber-coaxial (HFC) network segments during a 16-month period. Firstly, the study investigates the degree of unreliability in cable broadband networks by examining network layer packet loss resulting from physical layer transmission errors. We estimate that physical-layer errors can contribute to 12% to 25% of packet loss in the cable ISPs measured by the FCC's Measuring Broadband America project. Then, we propose CableMon, the first public-domain system that applies machine learning techniques to PNM data to improve the reliability of cable broadband networks. CableMon uses statistical models to generate features from time series data and uses customer trouble tickets as hints to infer abnormal thresholds for these generated features. Our results show that 81.9% of the abnormal events detected by CableMon overlap with at least one customer trouble ticket. This ticket prediction accuracy is four times higher than that of the existing public-domain tools used by ISPs. Last, we present TelAPart, a fault diagnosis system for cable networks, which can differentiate network faults caused by a faulty component inside a cable network and the network faults caused by a faulty component within a user's premise.