Why autonomous networks for CSPs and cable providers?
12๐ฆ๐๐บ๐บ๐ฎ๐ฟ๐: Manish Gupta, Head of Customer Engineering for Strategic Telecom Accounts at Google Cloud, introduces the Autonomous Network Operations framework. This framework brings together a a network digital twin, Graph Neural Networks (GNNs), and an agentic framework built on Google Cloud's planet-scale data and AI platforms to enable telecom and cable providers to automate operations, prevent outages, and improve customer experience.
๐๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ: Telecommunications operators face immense pressure from rising operational costs and increasing network demand. Legacy systems have created fragmented data silos, making it difficult to correlate network events with real-world customer impact. This leads to suboptimal customer experiences and hinders the ability to proactively manage network health. Furthermore, operators often lack the specialized in-house skills to build and maintain the sophisticated AI-driven automation platforms required to solve these modern challenges.
๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: Google Cloud's Autonomous Network Operations framework provides a comprehensive reference solution to accelerate automation. It unifies network data in BigQuery and builds a dynamic network digital twin using Spanner's graph capabilities. By applying advanced Vertex AI models, including Graph Neural Networks (GNNs), the framework generates deep insights and predictive recommendations. An integrated agentic framework allows network operations teams to interact with this complex data using natural language, dramatically simplifying troubleshooting and analysis.
๐ฅ๐ฒ๐๐๐น๐๐: By implementing the framework, operators can shift from reactive to proactive network management. The key outcomes include the ability to predict and prevent network outages, drastically shorten resolution times through real-time failure pinpointing, and optimize capital expenditures with intelligent capacity planning. Ultimately, ANO empowers operators to automate complex processes, reduce operational costs, and deliver a more reliable and responsive experience for their customers.
Key highlights:
โ โOperators are facing key challenges in the areas of network operations and planning. 1/ the increasing operational costs and the network demands; 2/ is suboptimal customer experience; and 3/ over a period of timeโฆ the data is quite fragmented.โ
โ โThe third major piece of this framework is the network digital twin that is going to represent the network topologyโฆ on top of this Spanner graph, we are going to apply the GNN algorithms, graph neural network algorithms, to create a deep learning about the graph.โ
โ Graph Neural Networks (GNNs) enable use cases such as predict and prevent network outages, shorten network outage time, real-time failure pinpointing, capital planning, capacity planning and optimization, root cause analysis, and take action on the network.โ
๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐น๐ผ๐๐ฑ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ ๐๐๐ฒ๐ฑ: BigQuery, Cloud Spanner, Vertex AI, Looker
๐๐ฒ๐ฎ๐ฟ๐ป ๐บ๐ผ๐ฟ๐ฒ:
โ Learn more about the Autonomous Network Operations framework: https://cloud.google.com/blog/topics/...
โ Learn about Google Chttps://cloud.google.com/blog/topics/telecommunications/the-autonomous-network-operations-framework-for-cspsud.google.com/solutions/te...
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