TinyAC: Bringing Autonomic Computing Principles to Resource-Constrained Systems
Networking and Internet Architecture
2025-09-25 v1
Abstract
Autonomic Computing (AC) is a promising approach for developing intelligent and adaptive self-management systems at the deep network edge. In this paper, we present the problems and challenges related to the use of AC for IoT devices. Our proposed hybrid approach bridges bottom-up intelligence (TinyML and on-device learning) and top-down guidance (LLMs) to achieve a scalable and explainable approach for developing intelligent and adaptive self-management tiny systems. Moreover, we argue that TinyAC systems require self-adaptive features to handle problems that may occur during their operation. Finally, we identify gaps, discuss existing challenges and future research directions.
Cite
@article{arxiv.2509.19350,
title = {TinyAC: Bringing Autonomic Computing Principles to Resource-Constrained Systems},
author = {Wojciech Kalka and Ruitao Xue and Kamil Faber and Aleksander Slominski and Devki Jha and Rajiv Ranjan and Tomasz Szydlo},
journal= {arXiv preprint arXiv:2509.19350},
year = {2025}
}