用可学习的小波卷积提升云工作负载预测精度与速度
SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces
- 将固定小波基改为可学习的卷积算子，自适应提取时序特征
- 通过多变量交互模块建模变量间空间关系，降低噪声干扰
- 相比现有方法误差降31%，延迟降80%，适合实时资源调度
准确的云工作负载预测对高效资源管理至关重要，但工作负载波动剧烈且易突发。尽管小波能保留时间局部性，但固定基函数难以捕捉复杂模式，且孤立处理忽略关键的空间依赖。为此，我们提出SWIFT，一种纯卷积框架，用于高效率工作负载预测。引入可学习级联小波路径，将传统固定小波基转化为自适应卷积算子，实现数据驱动的特征提取。同时，多变量交互模块依次建模变量间空间关系和变量内特征交互，稳定并优化噪声工作负载状态。大量实验表明，SWIFT达到当前最优性能，具有线性O(L)复杂度，在预测误差上最多降低31.04%，延迟减少79.74%。
原文摘要 · Abstract (English)
Accurate cloud workload forecasting is pivotal for efficient resource management but remains challenging as workloads are highly volatile and prone to sudden bursts. Although wavelets preserve temporal locality, rigid fixed bases struggle with complex patterns and isolated processing neglects critical spatial dependencies. To address this, we propose SWIFT, a pure convolutional framework designed for high-efficiency workload forecasting. We introduce a Learnable Cascaded Wavelet Path that reformulates the traditional fixed wavelet bases into adaptive convolutional operators, enabling precise, data-driven feature peeling. Complementing this, our Multivariate Interaction Module sequentially models inter-variable spatial and intra-variable feature interactions to stabilize and refine noisy workload states. Extensive experiments demonstrate that SWIFT achieves SOTA accuracy with linear O(L) complexity, reducing prediction error by up to 31.04% while cutting latency by 79.74%.
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