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Tiny machine learning models for autonomous workload distribution across cloud-edge computing continuum.

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Název: Tiny machine learning models for autonomous workload distribution across cloud-edge computing continuum.
Autoři: Pour-Hosseini, Mohammad R., Abbasi, Mahdi, Salimi, Atefeh, Elmroth, Erik, Haghighi, Hassan, Moradi, Parham, Javadi, Bahman
Zdroj: Cluster Computing; Oct2025, Vol. 28 Issue 6, p1-27, 27p
Témata: MACHINE learning, ARTIFICIAL intelligence, IMAGE processing, DECISION trees, RESOURCE allocation
Abstrakt: Resource management and task distribution in real-time have become increasingly challenging due to the growing use of latency-critical applications across dispersed edge-cloud infrastructures. Intelligent adaptable mechanisms capable of functioning effectively on resource-constrained edge devices and responding quickly to dynamic workload changes are required in these situations. In this work, we offer a learning-based system for autonomous resource allocation across the edge–cloud continuum that is both lightweight and scalable. Two models are presented: TinyDT, a small offline decision tree trained on state-action information retrieved from an adaptive baseline, and TinyXCS, an online rule-based classifier system that can adjust to runtime conditions. Both models are designed to operate on resource-constrained edge devices while minimizing memory overhead and inference latency. Our analysis demonstrates that TinyXCS and TinyDT outperform existing online and offline baselines in terms of throughput and latency, providing a reliable, power-efficient solution for next-generation edge intelligence. [ABSTRACT FROM AUTHOR]
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Abstrakt:Resource management and task distribution in real-time have become increasingly challenging due to the growing use of latency-critical applications across dispersed edge-cloud infrastructures. Intelligent adaptable mechanisms capable of functioning effectively on resource-constrained edge devices and responding quickly to dynamic workload changes are required in these situations. In this work, we offer a learning-based system for autonomous resource allocation across the edge–cloud continuum that is both lightweight and scalable. Two models are presented: TinyDT, a small offline decision tree trained on state-action information retrieved from an adaptive baseline, and TinyXCS, an online rule-based classifier system that can adjust to runtime conditions. Both models are designed to operate on resource-constrained edge devices while minimizing memory overhead and inference latency. Our analysis demonstrates that TinyXCS and TinyDT outperform existing online and offline baselines in terms of throughput and latency, providing a reliable, power-efficient solution for next-generation edge intelligence. [ABSTRACT FROM AUTHOR]
ISSN:13867857
DOI:10.1007/s10586-025-05289-x