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

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.

Authors 4

Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco, Massimo Coppola

Specification

Official page: arxiv.org/abs/2609.09348
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2609.09348
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.DC, cs.AI, cs.MA
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2609.09348
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.DC
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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T19

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2 h ago

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Authors: Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco

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Value	Valid from → to	Status	Source	Confidence	Extractor
2609.09348	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperSmart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management
New paper: Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 min ago	1

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