${\sf FinePar}$FinePar: A Fine-Grained Data Partitioning Framework for Cloud-Edge-Device Architectures

The Cloud-Edge-Device (CED) architecture has emerged as a new framework for real-time data processing in the Internet of Things (IoT) era. However, the edge and device face significant resource constraints that prevent them from storing or processing full datasets. Effective data partition across CED architectures is therefore critical for supporting real-time decision-making. However, existing static and coarse-grained dynamic methods fail to adapt to changing workloads and to meet real-time processing demands. To address this issue, we propose${\sf FinePar}$, a fine-grained dynamic data partitioning framework based on DRL, coupled with an efficient data allocation strategy.${\sf FinePar}$combines horizontal and vertical partition to optimize data partition across CED architectures to reduce data transfer volume and shorten execution time. We use DRL to adjust data partitioning strategies in real time based on task demands and resource states. To achieve end-to-end optimization, we design an efficient data allocation strategy. We verified the effectiveness of${\sf FinePar}$through extensive experiments. Experimental results show that${\sf FinePar}$can reduce the edge side latency by 80% under resource constraints and dynamically adapts to workload changes.