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Critical Infrastructure · Network Operations

Alarm Correlation & Network Fault Prediction

A national operator's network operations centre sees hundreds of thousands of alarms a day from radio, transport, core and fixed access, and engineers triage them by experience. Root causes are found after customers notice.

The challenge

What stands in the way

A national operator's network operations centre sees hundreds of thousands of alarms a day from radio, transport, core and fixed access, and engineers triage them by experience. Root causes are found after customers notice.
The solution

How Scrydon solves it

Alarms, performance counters and topology are fused against a model of the network, so a storm of alarms collapses into the one failing element that explains them. Predictive models flag degrading links and cells before they fail.
In practice

How this plays out

Alarm floods are a topology problem. When a fibre is cut, every service riding it raises its own alarm and the network operations centre sees a wall of red. The engineer's job is to find the one event that explains all of it, and today that depends on who is on shift.

The platform models the network as a graph of sites, elements, links and the services layered on top, and fuses alarms and performance counters onto it in real time. Correlation then becomes graph traversal: the alarms are grouped under the element whose failure explains them, and the affected services and customers are listed with them. Models trained on the operator's own history flag links and cells whose counters look like the days before previous failures.

Network topology and performance data describe critical infrastructure and are subject to NIS2 and national telecommunications law. The platform runs on the operator's own infrastructure, and the correlated view is produced without any of it leaving the network.

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The result
  • Fewer tickets, faster mean time to repair, and outages caught before they become NIS2 incidents.

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