Temperate Prediction in DC

Overall Structure

Top: CFD (Computational Fluid Dynamics) based approach Bottom: ML (Machine Learning) based approach

CFD Approach (Top)

• Basic Setup:
  • Spatial Definition & Material Properties: Physical space definition of the data center and material characteristics (servers, walls, air, etc.)
  • Boundary Conditions: Setting boundary conditions (inlet/outlet temperatures, airflow rates, heat sources, etc.)
• Processing:
  • Configuration + Physical Rules: Application of physical laws (heat transfer equations, fluid dynamics equations, etc.)
  • Heat Flow: Heat flow calculations based on defined conditions
• Output: Heat + Air Flow Simulation (physics-based heat and airflow simulation)

ML Approach (Bottom)

• Data Collection:
  • Real-time monitoring through Metrics/Data Sensing
  • Operational data: Power (Kw), CPU (%), Workload, etc.
  • Actual temperature measurements through Temperature Sensing
• Processing: Pattern learning through Machine Learning algorithms
• Output: Heat (with Location) Prediction (location-specific heat prediction)

Key Differences

CFD Method: Theoretical calculation through physical laws using physical space definitions, material properties, and boundary conditions as inputs ML Method: Data-driven approach that learns from actual operational data and sensor information for prediction

The key distinction is that CFD performs simulation from predefined physical conditions, while ML learns from actual operational data collected during runtime to make predictions.

With Claude