Abstract
Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform—efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.
Highlights
HFC (Hybrid Fiber–Coaxial) networks evolved from traditional cable TV networks to offer their users a broader spectrum of services, i.e., triple-play service.HFC networks employ DOCSIS (Data over Cable Service Interface Specification) standards.The DOCSIS 3.1 supports 10 Gbps downstream and 1 Gbps upstream [1]
We propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform
Given the trend of remote work, which has been further increased due to the COVID19 (Coronavirus Disease 2019) pandemic [3,4], it is of great importance that users are provided high network access availability
Summary
HFC (Hybrid Fiber–Coaxial) networks evolved from traditional cable TV networks to offer their users a broader spectrum of services, i.e., triple-play service (voice, data, video). Given the trend of remote work, which has been further increased due to the COVID19 (Coronavirus Disease 2019) pandemic [3,4], it is of great importance that users are provided high network access availability It is essential for telecom service providers to detect and localize malfunctions and failures in their networks as soon as possible. We propose a novel approach to efficiently detect and localize malfunctions and failures in HFC networks using a big data platform. In the remainder of the paper, we refer to our proposed approach as FDLBD—Failure Detection and Localization using Big Data platform.
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