Datasets:
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---
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}
}
```
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