Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management
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...
| Authors | Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco et al. |
|---|---|
| Primary category | cs.DC |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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