Datasets:

ArXiv:
License:
cloudops_tsf / README.md
gorold's picture
update README
0e2f653
|
raw
history blame
3.01 kB
metadata
license: cc-by-4.0
task_categories:
  - time-series-forecasting
pretty_name: cloud
size_categories:
  - 100M<n<1B

Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

Paper | Code

Datasets accompanying the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain".

from datasets import load_dataset

dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')

azure_vm_traces_2017

DatasetDict({
    train_test: Dataset({
        features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
        num_rows: 17568
    })
    pretrain: Dataset({
        features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
        num_rows: 159472
    })
})

borg_cluster_data_2011

DatasetDict({
    train_test: Dataset({
        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
        num_rows: 11117
    })
    pretrain: Dataset({
        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
        num_rows: 143386
    })
})

alibaba_cluster_trace_2018

DatasetDict({
    train_test: Dataset({
        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
        num_rows: 6048
    })
    pretrain: Dataset({
        features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
        num_rows: 58409
    })
})

Acknowledgements

The datasets were processed from the following original sources. Please cite the original sources if you use the datasets.

  • Azure VM Traces 2017

    • Bianchini. Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems Principles, pp. 153–167, 2017.
    • https://github.com/Azure/AzurePublicDataset
  • Borg Cluster Data 2011

    • John Wilkes. More Google cluster data. Google research blog, November 2011. Posted at http: //googleresearch.blogspot.com/2011/11/more-google-cluster-data.html.
    • https://github.com/google/cluster-data
  • Alibaba Cluster Trace 2018

    • Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, and Yungang Bao. Who limits the resource efficiency of my datacenter: An analysis of alibaba datacenter traces. In Proceedings of the International Symposium on Quality of Service, pp. 1–10, 2019.
    • https://github.com/alibaba/clusterdata

Citation

@article{woo2023pushing,
  title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain},
  author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen},
  journal={arXiv preprint arXiv:2310.05063},
  year={2023}
}