Intent-Aware Predictive Orchestration for Softwarized Networks Using LLM-Assisted Policy Translation

Abstract

: Softwarized networks have significantly improved programmability, elasticity, and service agility; however, network operations still depend heavily on expert-driven manual policy specification, particularly when translating high-level service intent into machine-executable orchestration actions. This gap becomes more pronounced in dynamic environments where traffic demand, resource pressure, and service-level objective violations evolve faster than rule-based operational workflows can respond. This paper presents an intent-aware predictive orchestration framework in which a large language model (LLM) serves as an intent-to-policy translation interface, while a predictive control layer estimates near-future service degradation risks and proactively selects orchestration actions. The proposed system consists of five coordinated modules: an intent input layer, an LLM-based intent parser, a policy validator with safety guardrails, a prediction engine for congestion and resource risk, and an orchestration layer for scaling, rerouting, and placement adaptation. Unlike works that employ generative models solely for textual assistance, the present study formulates the LLM as a constrained semantic compiler that maps operator intent to a structured network policy. The predictive layer jointly addresses three operational tasks: service-level agreement (SLA) violation prediction, link congestion prediction, and virtualized network function (VNF) saturation prediction. A benchmark-oriented evaluation is conducted against rule-based intent mapping, sequence labeling baselines, static threshold orchestration, and non-predictive reactive control. The proposed method achieves higher intent parsing accuracy, improved policy validity, lower SLA violation rate, and faster time-to-action than competitive baselines, while incurring bounded token and compute overhead. The results indicate that the practical value of LLMs in network softwarization is greatest when embedded in verifiable control loops rather than used as unconstrained generators.