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Cloud Workload Datasets Repository

This repository contains preprocessed workload datasets related to Google Cloud and Alibaba clusters. The datasets are organized into subfolders for each year and type of workload.

Folder Structure

  • GC11 (Google Cloud 2011)/

    • data/: Contains data related to Google Cloud workload for the year 2011.
    • preprocessing_scripts/: Contains preprocessing scripts for the Google Cloud 2011 data.
  • GC19 (Google Cloud 2019)/

    • machine-level/: Contains data and scripts specific to machine-level workload.
    • cluster-level/: Contains data and scripts specific to cluster-level workload. Each of which is divided in:
    • data/: Contains data related to Google Cloud workload for the year 2019.
    • preprocessing/: Contains preprocessing scripts for the Google Cloud 2019 data.
  • AC18 (Alibaba Cluster 2018)/

    • data/: Contains data related to Alibaba cluster workload for the year 2018.
    • preprocessing_scripts/: Contains preprocessing scripts for the Alibaba Cluster 2018 data.
  • AC20 (Alibaba Cluster 2020)/

    • data/: Contains data related to Alibaba cluster workload for the year 2020.
    • preprocessing_scripts/: Contains preprocessing scripts for the Alibaba Cluster 2020 data.

Usage

  • Clone this repository to your local machine.
  • Explore the data and use the preprocessing scripts as needed.

BibTeX Citation

If you use these preprocessed datasets in a scientific publication, we would appreciate using the following citations:

@inproceedings{rossi2022bayesian,
  title={Bayesian uncertainty modelling for cloud workload prediction},
  author={Rossi, Andrea and Visentin, Andrea and Prestwich, Steven and Brown, Kenneth N},
  booktitle={2022 IEEE 15th International Conference on Cloud Computing (CLOUD)},
  pages={19--29},
  year={2022},
  organization={IEEE}
}

@article{rossi2023uncertainty,
  title={Uncertainty-Aware Workload Prediction in Cloud Computing},
  author={Rossi, Andrea and Visentin, Andrea and Prestwich, Steven and Brown, Kenneth N},
  journal={arXiv preprint arXiv:2303.13525},
  year={2023}
}

@inproceedings{rossi2023clustering,
  title={Clustering-Based Numerosity Reduction for Cloud Workload Forecasting},
  author={Rossi, Andrea and Visentin, Andrea and Prestwich, Steven and Brown, Kenneth N},
  booktitle={International Symposium on Algorithmic Aspects of Cloud Computing},
  pages={115--132},
  year={2023},
  organization={Springer}
}

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