Contributed by Anodot
As the name suggests, Hybrid Fiber Coaxial (HFC) is by nature a hybrid technology composed of Optical and Radio Frequency technology delivered through a complex interaction of active and passive network elements. A typical deployment has thousands of street cabinets with the optical nodes, hundreds of hub sites, and hundreds of CMTS nodes. To add further complexity, the end-user Cable Modems, the passive end elements, are typically from multiple vendors, each with their own network management and performance management platforms, each with its own representation of performance.
A typical HFC network generates millions of metrics at various parts of the network. For example, a single cable modem can generate around 300 KPIs (there are millions of cable modems in the network), and thousands of additional metrics generated by the access, edge, core, backbone and interconnected layers.
This complexity is still considered a significant challenge for CSPs that rely on HFC expansion to scale their operations. As has been proven during the pandemic-induced usage spikes of the last year and a half, the built in flexibility and agility of HFC make it a prime candidate for delivering Gigabit internet speeds. Without proactive monitoring, however, the network is prone to service degradation and issues that take an exceedingly long time to detect and resolve. The NOC is often flooded with alarms to the extent of millions per day, and customer complaints trigger truck rolls that are often misdirected due to the lack of visibility and the lack of a common view between the NOC and the customer service teams. These dynamics often cause service disruptions that can take 48-72 hour spans to resolve. As a result, the operational costs of HFC are 10x compared to FTTx technologies.
Existing tools and techniques such as dashboards, static thresholds, and periodic reporting are often inadequate to detect issues quickly, identify the root cause, or offer any assistance in cutting the mean-time-to-repair (MTTR). For HFC networks, this is especially important in the context of slow leaks. Since very often failure indication takes time due to service or network element degradation, slow declines take long to reach the static threshold level. In addition, when subscribers experience uplink and downlink incidents such as throughput drops or packet loss there is no way to generate a coherent view of the root cause. This often leads to an increase in customer complaints, loss of brand reputation, and, more importantly, increased Opex costs.
To bring their HFC networks up to speed, leading CSPs are opting for zero touch machine learning based anomaly detection solutions. Cutting edge solutions enable NOCs to monitor the HFC networks across all metrics and dimensions, creating a holistic view of the network. By correlating between anomalies, these solutions provide the information needed for proactive detection and fast resolution of service issues, based on root cause analysis. Finally, by autonomously pinpointing network anomalies and correlating them, ML-based monitoring is paving the way for autonomous remediation. The technological roadmap is leading towards a fully automated ML remediation engine, that — based on detection and correlation — recommends an action based on previous actions, executes the action through the remediation engine, and fine tunes its operations through a closed feedback loop, increasingly improving its reactions.
Advanced zero touch network monitoring solutions give CSPs a holistic view across multi-vendor HFC environments for real time detection of service-impacting incidents. By monitoring network performance and service experience in real-time, solutions using this cutting edge technology provide near real-time detection of the incidents that impact the customers and bottom line. By using them as the brain on top of the OSS, CSPs can ensure customer satisfaction, minimize revenue loss, automate problem resolution — and reduce time to repair.
Anodot is realizing the autonomous network vision by providing CSPs with the ability to monitor service experience. We collect all data types, at any scale, and correlate anomalies across the entire telco stack.
Our end-to-end Service Experience Monitoring Platform detects service-impacting incidents in real time, helping customers like T-Mobile and Megafon reduce the number of alerts by 90% and shorten their Time to Resolve by 30%.