LLM-based policy generation for intent-based management of applications
Kristina Dzeparoska Jieyu Lin Ali Tizghadam Alberto Leon-Garcia
Automated management requires decomposing high-level user requests, such as
intents, to an abstraction that the system can understand and execute. This is
challenging because even a simple intent requires performing a number of
ordered steps. And the task of identifying and adapting these steps (as
conditions change) requires a decomposition approach that cannot be exactly
pre-defined beforehand. To tackle these challenges and support automated intent
decomposition and execution, we explore the few-shot capability of Large
Language Models (LLMs). We propose a pipeline that progressively decomposes
intents by generating the required actions using a policy-based abstraction.
This allows us to automate the policy execution by creating a closed control
loop for the intent deployment. To do so, we generate and map the policies to
APIs and form application management loops that perform the necessary
monitoring, analysis, planning and execution. We evaluate our proposal with a
use-case to fulfill and assure an application service chain of virtual network
functions. Using our approach, we can generalize and generate the necessary
steps to realize intents, thereby enabling intent automation for application
management.