Intent-Based Network in Online Resource Allocation With Machine-Learned Prediction

The development of Internet-of-Things (IoT) services demands intelligent and adaptive mechanisms for online resource allocation under dynamic and uncertain environments. Intent-Based Networking (IBN) has emerged as a promising paradigm to align system behavior with high-level user intents. However, realizing intent-aware allocation in real time remains challenging due to uncertain resource availability and incomplete future information. This paper presents a modular framework that integrates semantic intent parsing, machine-learned resource prediction, and robust online decision-making. We propose IBN-ONMP, an IBN-based online resource allocation algorithm that leverages machine-learned predictions and adapts safety margins based on feedback to ensure feasibility and performance under uncertainty. We formally define the problem, establish theoretical guarantees including regret and competitive ratio bounds, and validate the approach on real-world and simulated datasets. Experimental results demonstrate that IBN-ONMP achieves high utility and robust performance across varying prediction error levels, which is consistent with theoretical analysis.