Intent Assurance using LLMs guided by Intent Drift
This addresses the problem of maintaining intent conformance in dynamic networks for network management, representing an incremental improvement by applying existing LLM methods to a specific domain bottleneck.
The paper tackles the challenge of intent assurance in Intent-Based Networking by proposing a framework that detects and acts on intent drift, leveraging AI-driven policies generated by Large Language Models to assist with intent fulfillment and assurance.
Intent-Based Networking (IBN) presents a paradigm shift for network management, by promising to align intents and business objectives with network operations--in an automated manner. However, its practical realization is challenging: 1) processing intents, i.e., translate, decompose and identify the logic to fulfill the intent, and 2) intent conformance, that is, considering dynamic networks, the logic should be adequately adapted to assure intents. To address the latter, intent assurance is tasked with continuous verification and validation, including taking the necessary actions to align the operational and target states. In this paper, we define an assurance framework that allows us to detect and act when intent drift occurs. To do so, we leverage AI-driven policies, generated by Large Language Models (LLMs) which can quickly learn the necessary in-context requirements, and assist with the fulfillment and assurance of intents.