---
license: cc-by-4.0
---


# Dataset Description

This repository contains a dataset stored in **Parquet** format and partitioned by application tasks. The dataset is organized under the `parquet_ds/` directory, where each partition corresponds to a specific application task ID:  

```
parquet_ds/
 ├── tasks=4/
 ├── tasks=5/
 ├── tasks=6/
 ├── tasks=7/
 ├── tasks=9/
 ├── tasks=10/
 ├── tasks=11/
 ├── tasks=13/
 ├── tasks=14/
 ├── tasks=15/
 └── tasks=16/
```

## Task IDs and Applications

Each task ID represents one application type:

- **4**: Data Serving  
- **5**: Redis  
- **6**: Web Search  
- **7**: Graph Analytics  
- **9**: Data Analytics  
- **10**: MLPerf  
- **11**: HBase  
- **13**: Alluxio  
- **14**: Minio  
- **15**: TPC-C  
- **16**: Flink  

## Loading the Dataset

You can easily load the dataset with [PyArrow](https://arrow.apache.org/docs/python/):

```python
import pyarrow.dataset as ds

dataset = ds.dataset("parquet_ds", format="parquet", partitioning="hive")
print(dataset.schema)
```

## Schema

Each task ID represents one application type:

- **perf_ori (double)**: Normalized performance level between 0 and 1.
- **workload (double)**: Numerical identifier assigned to workload level.  
- **tr_self**: VM metrics for the target application.
- **lin_self**: Linux Perf metrics for the target application.
- **td_self**: Top-Down analysis for the target application.  
- **tr_oth**: VM metrics for co-located neighbor VMs.  
- **lin_oth**: Linux Perf metrics for neighbor VMs.  
- **td_oth**: Top-Down analysis for neighbor VMs.  
- **tasks (int32)**: Application task ID (partition key).

## Schema

Total coverage: 317 days of traces.


## Citation

If you use this dataset in your research, please cite it as:

```
@misc{cloudformer2025,
  title        = {CloudFormer: An Attention-based Performance Prediction for Public Clouds with Unknown Workload},
  author       = {Shahbazinia, Amirhossein and Huang, Darong and Costero, Luis and Atienza, David},
  howpublished = {arXiv preprint arXiv:2509.03394},
  year         = {2025},
  url          = {https://arxiv.org/abs/2509.03394}
}
```