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.

Large telecom-service-provider networks are typically based on complex communications infrastructures comprising millions of network devices. The performance monitoring of such networks is a very demanding and challenging task. A large amount of data is collected and processed during performance monitoring to obtain information that gives insights into the current network performance. Using the obtained information, providers can efficiently detect, locate, and troubleshoot weak spots in the network and improve the overall network performance. Furthermore, the extracted information can be used for planning future network expansions and to support the determination of business-strategy decisions. However, traditional methods for processing and storing data are not applicable because of the enormous amount of collected data. Thus, big-data technologies must be used. In this paper, a big-data platform for the performance monitoring of telecom-service-provider networks is proposed. The proposed platform is capable of collecting, storing, and processing data from millions of devices. Typical challenges and problems in the development and deployment process of the platform, as well as the solutions to overcome them, are presented. The proposed platform is adjusted to HFC (Hybrid Fiber-Coaxial) network and currently operates in the real HFC network, comprising millions of users and devices.

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