This talk outlines a holistic framework for network digital twins (NDTs) in high-performance computing, covering construction, optimization, and application. The approach integrates federated learning for privacy-preserving model updates and reinforcement learning for closed-loop control, enabling real-time adaptation to traffic and system dynamics.
Topics Covered
- HPC network digital twin construction: topology and telemetry ingestion, calibration
- Distributed and regional twin orchestration
- Federated learning for privacy-preserving NDT updates
- Reinforcement learning for closed-loop HPC network control
- Case studies: workload forecasting, congestion-aware routing, anomaly detection
- Portability to edge and 6G-class environments