Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management

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.

This is the Accepted Manuscript, after peer review, not the Version of Record; no post-acceptance changes. Presented at FRAME 2026 (Euro-Par 2026). Subject to Springer's AM terms of use: this https URL

Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2609.09348 [cs.DC]

(or arXiv:2609.09348v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2609.09348 arXiv-issued DOI via DataCite (pending registration)