提出双输入时空频模型，精准预测云边协同下的多视角负载变化
DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling
- 双输入架构同时建模时序、空间与频域特征
- 在CPU和TP数据集上多时段预测均优于主流方法
- 适合需要实时响应的云边协同系统负载预测
随着边缘侧AI推理的广泛应用，边缘平台需支持低延迟、高并发且可靠性要求高的应用。然而，现有方法在云边协同环境中难以兼顾多维特征建模与预测效率。为此，我们提出DSTFView，一种面向云边协同环境的双输入时空频多视角负载预测框架。该框架联合建模紧密性与周期性依赖，并提取空间、时间及频域依赖关系。此外，设计自适应融合机制，动态调整各视图贡献以捕捉突变情况。在CPU和TP数据集上的实验表明，DSTFView在多个预测时长和评估指标下持续优于代表性基线方法。
原文摘要 · Abstract (English)
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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