Fremer (VLDB 2025 Paper)

Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services.

Performance

Comparison of throughput and prediction error among different Transformers on IaaS workload dataset:

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Forecasting results on distinct workload series datasets:

Usage

1. Installation

Install the required dependencies using the following command:

pip install -r requirements.txt

2. Prepare the Data

• Download the dataset from https://drive.google.com/file/d/1vgpOmAygokoUt235piWKUjfwao6KwLv7/view?usp=drive_link provided by TFB. Then place the downloaded data under the folder ./dataset.

• Obtained the ByteDance Workload Data from https://huggingface.co/datasets/ByteDance/CloudTimeSeriesData. Then put the data under ./dataset/forecasting.

3. Run the Scripts under ./scripts/multivariate_forecast

For example:

bash ./scripts/multivariate_forecast/RDS_script/Fremer.sh

Citation

If you find this work useful, please cite our paper:

@article{ye2025fremer,
  title   = {Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services},
  author  = {Hengyu Ye and Jiadong Chen and Fuxin Jiang and Xiao He and Tieying Zhang and Jianjun Chen and Xiaofeng Gao},
  journal = {Proceedings of the VLDB Endowment},
  volume  = {18},
  number  = {11},
  pages   = {3812--3825},
  year    = {2025}
}

Acknowledgement

• We extend our gratitude to TFB (https://github.com/decisionintelligence/TFB) for their support in the evaluation phase of this study.

• This research is supported by ByteDance, and we deeply appreciate their contributions.