README.md

---
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](https://arxiv.org/abs/2310.05063) | [Code](https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops)

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

## Quick Start

```bash
pip install datasets==2.12.0 fsspec==2023.5.0
```

### azure_vm_traces_2017
```python
from datasets import load_dataset

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

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
```python
dataset = load_dataset('Salesforce/cloudops_tsf', 'borg_cluster_data_2011')
print(dataset)

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
```python
dataset = load_dataset('Salesforce/cloudops_tsf', 'alibaba_cluster_trace_2018')
print(dataset)

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
    })
})
```

## Dataset Config
```python
from datasets import load_dataset_builder

config = load_dataset_builder('Salesforce/cloudops_tsf', 'azure_vm_traces_2017').config
print(config)

CloudOpsTSFConfig(
    name='azure_vm_traces_2017',
    version=1.0.0,
    data_dir=None,
    data_files=None,
    description='',
    prediction_length=48,
    freq='5T',
    stride=48,
    univariate=True,
    multivariate=False,
    optional_fields=(
        'feat_static_cat',
        'feat_static_real',
        'past_feat_dynamic_real'
    ),
    rolling_evaluations=12,
    test_split_date=Period('2016-12-13 15:55', '5T'),
    _feat_static_cat_cardinalities={
        'pretrain': (
            ('vm_id', 177040),
            ('subscription_id', 5514),
            ('deployment_id', 15208),
            ('vm_category', 3)
        ),
        'train_test': (
            ('vm_id', 17568),
            ('subscription_id', 2713),
            ('deployment_id', 3255),
            ('vm_category', 3)
        )
    },
    target_dim=1,
    feat_static_real_dim=3,
    past_feat_dynamic_real_dim=2
)
```
```test_split_date``` is provided to achieve the same train-test split as given in the paper.
This is essentially the date/time of ```rolling_evaluations * prediction_length``` time steps before the last time step in the dataset.
Note that the pre-training dataset includes the test region, and thus should also be filtered before usage.

## 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
<pre>
@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}
}
</pre>

## Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.