Implementation Details
Performance Characteristics
- Latency: Sub-100ms processing for typical workloads.
- Throughput: 1000+ windows/second on modern hardware.
- Memory: Bounded state. We strictly limit the history size to prevent OOM.
- Scalability: Horizontal scaling via federation. Add more nodes to increase capacity.
Resource Requirements
- Minimum: 4GB RAM, 2 CPU cores.
- Recommended: 16GB RAM, 8 CPU cores.
- GPU: Optional. CUDA toolkit required if enabled.
- Storage: Configurable cache size (typically <1GB).
Error Handling
The system is designed for Resilience:
- Graceful Degradation If the GPU fails, the system falls back to CPU automatically.
- Error Isolation A crash in the Federation layer does not stop the Inference Engine.
- Automatic Recovery Components retry connections with exponential backoff.
Extensibility
You can extend the framework in 4 ways:
- Custom Operators: Add new sketch functions.
- Custom Policies: Write your own DRG rules (e.g., "Stop if Battery < 20%").
- Custom Sources: Adapter for your specific data stream.
- Custom Aggregators: Implement new Federated Learning averaging strategies.