The cloud native ecosystem has seen a surge in innovation, driven by machine learning and artificial intelligence (ML/AI). These advancements provide real-time insights to customers, enhancing the value of cloud services. In contrast, networking has not kept pace with this level of innovation, lacking significant advancements in network services.
In order to establish an ML/AI-powered network, it is essential to adhere to several foundational principles outlined as follows:
Automate by simplifying
- Standardize elements and variables to reduce complexity in automation
- Homogeneity in lower stack levels makes innovation easier in higher layers
- Adopt common end-to-end architecture and management interfaces for efficient automation and innovation
Big data paves the way for ML/AI
- Develop a Google-Maps-For-Networks approach for data collection and analysis
- Expand network telemetry data beyond hardware, policy, and protocol counters
- Utilize demand-driven network services for tailored network experiences
Innovate by automating, then innovate further using ML/AI
- Transform networks into agile platforms for operator and customer innovation
- Simplify network infrastructures and interfaces to reduce complexity
- Collect normalized network data and house it in a big data system for service innovation and ML/AI integration
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