AGENTIC-SYSTEMSJuly 28, 20261 min read

DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling

Researchers have proposed DSTFView, an innovative dual-input spatio-temporal-frequency framework designed for multi-view workload forecasting in cloud-edge collaborative environments. Experimental results demonstrate that DSTFView outperforms baseline models across multiple forecasting horizons by effectively capturing abrupt workload changes.

Computer Science > Artificial Intelligence

Title:DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling

View PDF HTML (experimental)Abstract:With the widespread deployment of edge-side AI inference, edge platforms are increasingly required to support latency-sensitive, highly concurrent, and reliability-critical applications. However, existing methods often struggle to balance multidimensional feature modeling and forecasting efficiency in collaborative cloud-edge environments. To address this issue, we propose DSTFView, a dual-input spatio-temporal-frequency multi-view workload forecasting framework for collaborative cloud-edge environments. It jointly models closeness and period dependencies and extracts spatial, temporal, and frequency-domain dependencies. Besides, it designs an adaptive fusion mechanism and adjusts the contribution of each view to capture abrupt changes. Experimental results on the CPU and TP datasets demonstrate that DSTFView consistently outperforms representative baselines across multiple forecasting horizons and evaluation metrics.

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Source: arXiv cs.AI Recent

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