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arXiv · 2605.09623

Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum

Akuen Akoi Deng·Eimantas Butkus·Alfreds Lapkovskis·Praveen Kumar Donta
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Abstract

In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to AI task partitioning and offloading across the edge-cloud continuum typically rely on static methods that ignore runtime dynamics. Furthermore, they are often evaluated in simulated environments rather than on real hardware. To address this gap, we propose a framework that dynamically splits neural network layers across the heterogeneous continuum. The framework profiles the model at startup, measures network link conditions between nodes, and periodically re-evaluates the partition to adapt to environmental changes. We created a physical testbed comprising a Raspberry Pi edge device, a laptop fog, and a high-performance desktop PC as the cloud. We evaluated the framework over three widely adopted convolutional neural networks: VGG16, AlexNet, and MobileNetV2. Our results show that the framework achieves reductions in energy and end-to-end latency of 27.09--35.82% and 6.34--22.92%, respectively, compared to a static partitioning baseline. These findings confirm the superiority of adaptive to static partitioning.

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BibTeXRIS

Akuen Akoi Deng, Eimantas Butkus, Alfreds Lapkovskis, Praveen Kumar Donta. 2026-08-18. Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum. https://doi.org/10.1007/978-3-032-35576-8_23

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