mtbenchmark 1.0.0

A time-series prediction benchmarking tool tailored to enterprise scenarios.

MTB: A Time-Series Prediction Benchmarking Tool Tailored to Enterprise Scenarios

Introduction

MTB is a time-series prediction benchmarking tool tailored to enterprise scenarios. MTB establishes prediction performance evaluation standards that align with enterprise expectations, enabling a fair comparison of time-series prediction algorithms based on their performance with real enterprise traffic data.

Quickstart

1. Install Dependencies: Ensure you have Python and pip installed. Then, install the required dependencies using:

   pip install -r requirements.txt
   

2. Load Data: MTB implements the PyTorch dataset interface, and you can load data using the following code. For more details, refer to example.py:

   The data will be automatically downloaded from Google Drive on the first run, or you can manually download it: link.

   train_dataset = mt_dataset.MTDataset(features='svc1', split="train")
   val_dataset = mt_dataset.MTDataset(features='svc1', split="val")
   test_dataset = mt_dataset.MTDataset(features='svc1', split="test")
   

Cite

If you find this repo useful, please cite our paper.

@inproceedings{guo2024pass,
  title={PASS: Predictive Auto-Scaling System for Large-scale Enterprise Web Applications},
  author={Guo, Yunda and Ge, Jiake and Guo, Panfeng and Chai, Yunpeng and Li, Tao and Shi, Mengnan and Tu, Yang and Ouyang, Jian},
  booktitle={Proceedings of the ACM on Web Conference 2024},
  pages={2747--2758},
  year={2024}
}

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

• Share: Copy and redistribute the dataset in any medium or format.
• Adapt: Remix, transform, and build upon the dataset for any purpose, even commercially.

Attribution Requirement:
If you use this dataset, please cite it as follows:

Dataset Name, Author Name(s), Year. Licensed under CC BY 4.0. [Link to your repository or DOI].

For full license details, see CC BY 4.0 License.