Not All Frequencies Are Equal: Asymmetric Spectral Modeling for Cloud Workload Forecasting

Workload forecasting is essential for cloud resource management, yet real-world workloads often exhibit complicated periodic patterns across multiple temporal scales. Although frequency-domain forecasting provides an effective representation for such patterns, existing methods commonly apply homogeneous transformations across the spectrum, overlooking the distinct structures of low- and high-frequency components. To address this issue, we propose DuoFreq, a lightweight frequency-domain forecasting framework based on Dual-Band Spectral Modulation (DBSM). DuoFreq separates the spectrum into low- and high-frequency bands and models them with specialized mechanisms. Smooth Low-Frequency Modulation (SLFM) preserves and adapts low-frequency structures through smooth residual modulation, while Local-Global High-Frequency Modulation (LG-HFM) captures neighboring and band-wide dependencies, together with Complex Spectral Transformation (CST) for complex-valued high-frequency modeling. Extensive experiments on five real-world cloud workload datasets show that DuoFreq achieves the best MSE in 17 of 20 forecasting settings and the best MAE in 18, with average reductions of 9.48% and 7.09%, respectively. DuoFreq also generalizes well to unseen workload instances while maintaining a favorable accuracy–efficiency trade-off with approximately 0.61M parameters.