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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]
Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Tiny machine learning models for autonomous workload distribution across cloud-edge computing continuum.
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  Data: 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]
– Name: Abstract
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  Data: <i>Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Oct2025
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