Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services
Summary: Fremer: a frequency-domain lightweight Transformer for cloud workload forecasting that leverages periodicity to cut compute/parameters while boosting accuracy and multi-period robustness. Beats SOTA on public plus four ByteDance datasets (≈5.5% MSE, 4.7% MAE, 8.6% SMAPE) and improves Kubernetes auto-scaling (≈18.8% latency, 2.35% resource savings). (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Hengyu Ye
- 2. Jiadong Chen
- 3. Fuxin Jiang
- 4. Xiao He
- 5. Tieying Zhang
- 6. Jianjun Chen
- 7. Xiaofeng Gao
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,298 | TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods | 2024 | VLDB | 9.0742746e-05 |
| 8,286 | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | 2023 | VLDB | 4.5435639e-05 |
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