Large Test Case Data Files Solution

## Background
Our test data files fall into two categories:
1. **Regular Files (<100MB)**: Stay in git repository
2. **Large Files (>100MB)**: Need alternative storage solution

Example large file from `testdata.webkit.json`:
```json
{
"josh_ios15_ffs": {
"artifacts": {
"webkitCacheRecords": {
"file_count": 4137,
// Creates a 300MB zip file - too large for regular git
}
}
}
}
```

## Storage Options for Large Files (>100MB)

### 1. GitHub LFS
- **Cost**: Free tier - 1GB storage/bandwidth per month
- **Pros**:
- Native git integration
- Automatic with GitHub Actions
- Version controlled
- **Cons**:
- Limited free tier
- Additional bandwidth/storage = $5/50GB/month

### 2. Azure Blob Storage
- **Cost**: ~$0.25/month for 25GB
- **Pros**:
- Cost-effective
- Reliable
- Easy GitHub Actions integration
- Team maintains control
- **Cons**:
- Not free
- Requires setup/maintenance

### 3. Google Drive
- **Cost**: Free tier - 15GB
- **Pros**:
- Free storage
- Familiar interface
- **Cons**:
- API setup required
- More complex GitHub Actions integration
- Manual version control

### 4. GitHub Releases
- **Cost**: Free
- **Pros**:
- 2GB file limit
- No bandwidth limits
- Native to GitHub
- **Cons**:
- 10GB total limit
- Manual upload process
- Less automated

## Questions for Discussion
1. Is cost or automation more important?
2. Do we anticipate many large test files?
3. How important is version control for test data?
4. Who will maintain the storage solution?
5. Are there other cloud services to consider?

El issue analiza soluciones de almacenamiento para archivos de prueba grandes (>100MB), como los referenciados en testdata.webkit.json. La investigación incluye evaluar GitHub LFS, Azure Blob Storage, Google Drive y GitHub Releases en cuanto a coste, automatización e integración con GitHub Actions. El trabajo requiere comprender la estructura de los datos de prueba del proyecto e implementar una solución elegida, probablemente mediante configuración y scripts.

Escrito por el modelo de indexación a partir del texto del issue.