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

AI 中文解读

DSTFView框架为云边缘协同系统装上了“预测雷达”。这项技术专门解决一个现实难题：当大量AI应用在边缘设备上实时运行时，传统方法要么预测不准，要么反应太慢。DSTFView的核心创新是双管齐下，它同时分析数据的“规律性”（比如每日高峰）和“突发性”（比如瞬间流量激增），像天气预报一样提前告诉系统“接下来几分钟服务器要忙了”。实验显示，无论是CPU还是其他资源预测，它的表现都显著优于现有方案。这意味着以后你刷手机上的AI修图、智能客服或自动驾驶服务时，后台的资源调度会变得更聪明——系统能提前扩充算力、合理分配任务，从而避免卡顿或掉线。对普通用户来说，技术不必懂，但能用上更流畅、更可靠的智能服务。

arXiv:2607.22565v1 Announce Type: new 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.