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
```python
from datasets import load_dataset

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

## Available Datasets
### azure_vm_traces_2017
```python
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
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
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='alibaba_cluster_trace_2018',
    version=1.0.0,
    data_dir=None,
    data_files=None,
    description='',
    prediction_length=48,
    freq='5T',
    stride=48,
    univariate=False,
    multivariate=True,
    optional_fields=('feat_static_cat', 'past_feat_dynamic_real'),
    rolling_evaluations=12,
    test_split_date=Period('2018-01-08 11:55', '5T'),
    _feat_static_cat_cardinalities={
        'pretrain': (
            ('container_id', 64457),
            ('app_du',9484)),
        'train_test': (
            ('container_id', 6048),
            ('app_du', 1292)
        )
    },
    target_dim=2,
    feat_static_real_dim=0,
    past_feat_dynamic_real_dim=6
)
```
```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
```
@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}
}
```