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.

Storage scheduling is crucial for time series storage. However, designing an efficient hot and cold tiered storage scheduling strategy for time series across Cloud-Edge-Device (CED) architecture remains challenging. Although numerous research have studied hot and cold classification for relational data, these methods are not suitable for time series which has strong timeliness and complex access patterns. Therefore, in this paper, we present TempSched, a temperature-aware storage scheduler for time series across CED, which can identify hot and cold time series and predict data temperature efficiently to perform storage scheduling in advance. By employing Newton's law of cooling and the thermal radiation law, TempSched establishs a temperature model and encapsulates data temperature. It supports classifying hot and cold data and scheduling time series across CED. Subsequently, TempSched designs a workload prediction model and a frequent timestamp discovery algorithm to forecast access patterns and predict the future temperature. This can timely adjust to hot and cold storage. We validate TempSched on a public dataset, and the experimental results show that it can achieve about 94% hit rate for data access on the edge and device, which is 12% better than existing methods. It can help CED avoid storage overhead caused by storing the full data at all three sides, and greatly reduce data transfer overhead.