Sudhir Rustogi


Techniques are described herein for using semi-supervised machine learning to simplify an intent interface for end users by allowing a user to specify key network features in which they are interested. A continual learning based approach better adapts to a continuously changing intent interface and simplifies the experience for end users. The semi-supervised learning algorithm learns the reverse mapping (stored in an intent cache database) of Group-Based Policy (GBP) policy templates expressed using data models (e.g., as Yang models) as well as user network feature key words given a set of existing network configuration use cases provided as topology network maps, device configurations, and manually crafted GBP policy objects. A new user starts by specifying key intent features of interest, picks the closest mapping GBP template, and configures their network.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.