Semantic-Aware LLM Orchestration for Proactive Resource Management in Predictive Digital Twin Vehicular Networks

TL;DR

This paper introduces a proactive, semantic-aware orchestration framework using large language models and predictive digital twins to optimize resource management in dynamic vehicular networks, outperforming reactive methods.

Contribution

It proposes transforming digital twins into predictive models and leveraging LLMs for proactive, goal-driven network management in vehicular edge computing environments.

Findings

SP-LLM outperforms reactive approaches in scalability and robustness.

The framework effectively interprets natural language commands for dynamic policy adjustment.

Simulation results demonstrate significant improvements in network performance.

Abstract

Next-generation automotive applications require vehicular edge computing (VEC), but current management systems are essentially fixed and reactive. They are suboptimal in extremely dynamic vehicular environments because they are constrained to static optimization objectives and base their decisions on the current network states. This paper presents a novel Semantic-Aware Proactive LLM Orchestration (SP-LLM) framework to address these issues. Our method transforms the traditional Digital Twin (DT) into a Predictive Digital Twin (pDT) that predicts important network parameters such as task arrivals, vehicle mobility, and channel quality. A Large Language Model (LLM) that serves as a cognitive orchestrator is at the heart of our framework. It makes proactive, forward-looking decisions about task offloading and resource allocation by utilizing the pDT's forecasts. The LLM's ability to decipher high-level semantic commands given in natural language is crucial because it enables it to dynamically modify its optimization policy to match evolving strategic objectives, like giving emergency services priority or optimizing energy efficiency. We show through extensive simulations that SP-LLM performs significantly better in terms of scalability, robustness in volatile conditions, and adaptability than state-of-the-art reactive and MARL-based approaches. More intelligent, autonomous, and goal-driven vehicular networks will be possible due to our framework's outstanding capacity to convert human intent into optimal network behavior.