发表机构
University of Science and Technology Beijing（北京科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对万物互联的动态资源调度挑战，提出大语言模型驱动的资源分配方案，构建多维调度决策模型并引入反馈模块，仿真显示其在收敛速度等指标上表现优异。
AI 中文摘要
万物互联（Internet of Everything, IoE）的快速发展推动了智能应用的普及，但海量连接设备产生的多样异构任务，给IoE环境下的动态资源调度带来了日益严峻的挑战。大型人工智能模型（Large Artificial Intelligence Models, LAIM）凭借卓越的语义理解与推理能力，在应对复杂调度场景、提升资源利用效率方面展现出巨大潜力。本文研究了一种面向任务的LAIM驱动资源调度机制，通过融合任务语义、网络状态与约束条件构建多维调度决策模型；还设计了面向任务的提示生成方法，建立任务需求与网络状态的深度关联。所提资源分配方案中引入外部评估与反馈模块，对调度策略进行实时可行性验证与性能评估，以增强调度的鲁棒性与适应性。仿真结果表明，所提大语言模型（Large Language Model, LLM）驱动的网络架构与资源分配方案在收敛速度、处理延迟和能耗方面均实现显著提升，有效增强了IoE任务响应能力与资源利用率。
英文摘要
The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications. However, the massive number of connected devices generates diverse and heterogeneous tasks, which pose increasing challenges for dynamic resource scheduling in IoE environments. Using their superior semantic understanding and reasoning capabilities, Large Artificial Intelligence Models (LAIMs) demonstrate significant potential to handle complex scheduling scenarios and improve resource utilization efficiency. This paper investigates a task-oriented LAIM-driven resource scheduling mechanism, which constructs a multidimensional scheduling decision model by integrating task semantics, network states, and constraint conditions. Furthermore, a task-oriented prompt generation method is designed to establish a deep association between task requirements and network state. In the proposed resource allocation scheme, an external evaluation and feedback module is incorporated to conduct real-time feasibility verification and performance evaluation of scheduling strategies, thus enhancing the robustness and adaptability of scheduling. Simulation results demonstrate that the proposed Large Language Model (LLM)-driven network architecture and resource allocation scheme achieve significant improvements in convergence speed, processing latency, and energy consumption, effectively enhancing IoE task responsiveness and resource utilization.
