Beyond blueprints that provide best practices and deployment guidance, there are specific innovations inside of a series of LF Networking projects. Joshipura noted that projects like Nephio simplify the deployment of cloud-native network functions with a declarative approach to service description and intent-based automation that converts operators’ desired state of the network to actual configuration tasks.
Additionally the Cloud-Native Telecom Initiative (CNTi) project creates definitions for best practices for developing and deploying cloud-native network functions (CNFs) and test frameworks that validate the proper use of the best practices.
“This helps operators staff successfully execute complex, cloud-native service deployment tasks, even if they did not initially have deep expertise in this domain and have legacy systems,” Joshipura said. “LF also has a wide variety of e-learning courses for cloud-native and Kubernetes, and we have seen quite a steep response to these for upskilling within the community.”
New AI projects address ethics and network-specific challenges
The two new AI initiatives—Salus and Essedum—represent a strategic push into domain-specific AI for networking, with both projects built on code donated by Infosys.
Joshipura noted that a lot of people in networking organizations are going to be consumers of the same data and models through different AI-enabled applications. To make sure that there is right and responsible use of AI in these applications, organizations need AI guardrail frameworks. That is the key issue Salus is addressing.
“Salus is a framework that brings in AI guardrails on top of the data and models, which ensures enhanced security, data privacy and traceability and prevents sensitive issues like biases,” explained Joshipura. “For networks, this is relevant because it’s becoming clearer that AI for networks needs centralized and uniform data and model strategy.”
