Improving the Reliability of Cable Broadband Networks via Proactive Network Maintenance
Jiyao Hu, Zhenyu Zhou, Xiaowei Yang

TL;DR
This paper introduces CableMon, a machine learning-based system that improves cable broadband network reliability by accurately detecting failures and distinguishing their causes using proactive maintenance data.
Contribution
CableMon is the first public-domain system applying machine learning to PNM data for failure detection and localization in cable networks, addressing high false-positive issues.
Findings
CableMon effectively detects failures using PNM data.
It distinguishes between network and subscriber failures.
Outperforms existing public tools in accuracy.
Abstract
Cable broadband networks are one of the few "last-mile" broadband technologies widely available in the U.S. Unfortunately, they have poor reliability after decades of deployment. The cable industry proposed a framework called Proactive Network Maintenance (PNM) to diagnose the cable networks. However, there is little public knowledge or systematic study on how to use these data to detect and localize cable network problems. Existing tools in the public domain have prohibitive high false-positive rates. In this paper, 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 tackles two key challenges faced by cable ISPs: accurately detecting failures, and distinguishing whether a failure occurs within a network or at a subscriber's premise. CableMon uses statistical models to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPower Line Communications and Noise · Power System Reliability and Maintenance
