Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum
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...
| Authors | Akuen Akoi Deng, Eimantas Butkus, Alfreds Lapkovskis et al. |
|---|---|
| Primary category | cs.DC |
| Published | 10 May 2026 |
| Last updated at source | 18 August 2026 |
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