发表机构
Xinjiang University（新疆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对协作式云边环境中多维特征建模与预测效率平衡难题，提出DSTFView框架，通过双输入时空频率建模联合捕捉多种依赖性，设计自适应融合机制，实验证明其在多指标上优于基线。
AI 中文摘要
随着边缘侧人工智能推理的广泛部署，边缘平台越来越需要支持对延迟敏感、高并发且对可靠性要求高的应用程序。然而，现有方法在协作式云边环境中往往难以平衡多维特征建模和预测效率。为解决此问题，我们提出了DSTFView，这是一种用于协作式云边环境的双输入时空频率多视图工作负载预测框架。它联合对紧密性和周期依赖性进行建模，并提取空间、时间和频域依赖性。此外，它设计了一种自适应融合机制，并调整每个视图的贡献以捕获突变。在CPU和TP数据集上的实验结果表明，DSTFView在多个预测范围和评估指标上始终优于代表性基线。
英文摘要
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.
CommentsAccepted in WASA 2026
