Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum
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Abstract - In recent years, the use of artificial intelligence on resourceconstrained IoT devices has grown significantly. However, existing approaches to DNN 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 highperformance 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.
Recommended citation: Deng, A. A., Butkus, E., Lapkovskis, A., & Donta, P. K. (2026). Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum. arXiv preprint arXiv:2605.09623.
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