Google Cloud unveils AI framework for telecoms networks
Google Cloud has outlined an
Autonomous Network Operations framework for telecommunications providers that
combines a network digital twin, graph-based machine learning and AI agents.
The approach is intended to help
operators manage increasingly complex networks that generate vast amounts of
operational data and are becoming harder to run through manual processes and
conventional machine learning models alone.
The framework has three main
layers: a digital twin held in Spanner Graph, a machine learning layer built on
Distributed Graph Flow, and an AI layer designed to interpret outputs and take
action. It is intended to support network operations tasks including anomaly
detection, root cause analysis, capacity planning, traffic forecasting and
scenario testing.
At the centre of the design is a
digital representation of a live telecoms network. Google Cloud describes this
as a temporal graph that reflects changes in the network over time, allowing
operators to analyse both current and historical conditions.
The structure is intended to map
how routers, interfaces, VPNs and traffic flows relate to one another. It also
records links between physical equipment, control-plane relationships, service
membership and traffic anchoring, giving machine learning models and AI agents
a shared operational picture of the network.
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