Problem
Research questions and friction points this paper is trying to address.
resource management
IoT-Edge-Cloud
deep reinforcement learning
large language models
feedback loop
Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management
🤖 AI Summary
研究通过扩展DRL技术分类并引入AI增强范式和反馈通道维度，解决IoT-边缘-云资源管理中的动态决策问题。
📝 Abstract
Managing resources across IoT, edge, and cloud layers calls for continuous, context-aware decisions under constraints that rarely stay fixed. Deep reinforcement learning (DRL) handles this class of problems well, and large language models (LLMs) are increasingly used to augment DRL pipelines, yet the architectural relationship between the two is seldom made explicit. We build on Wang et al.'s taxonomy of Continuum Orchestration Systems employing DRL techniques and extend it with two further dimensions. The AI Augmentation Paradigm measures how LLMs are exploited, while the Feedback channel captures whether and through which system path the execution feedback returns to the LLM in order to close the MAPE control loop at the LLM Orchestration layer. We apply this taxonomy to six recent system architectures and find a common gap, as none combines full LLM orchestration with full agent-layer feedback in a Cloud Continuum setting. We relate this gap to a missing cross-tier feedback abstraction, bridging the incommensurable per-tier signals and the LLM Orchestrator.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Reinforcement Learning
Large Language Models
AI Augmentation Paradigm
Feedback Channel
Cross-tier Feedback Abstraction
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