A software platform for data center control and optimization that uses a thermo-fluid dynamic Digital Twin to improve energy efficiency of IT infrastructure and cooling systems. Reduce consumption, improve PUE, and prevent thermal hotspots, while maintaining SLA compliance.
| Metric | Result |
|---|---|
| Energy savings | >10% |
| Power Usage Effectiveness | Improved |
| Thermal hotspots | 0 |
At the core of DCIMate is a digital twin that reproduces the thermo-fluid dynamic behavior of the physical environment. It combines discrete CFD simulation for airflow and heat distribution and cooling system models representing CRAC/CRAH units and ventilation systems.
This makes it possible to simulate temperature evolution at the rack level and evaluate the thermal impact of variations in computational workload, in near real time.
Full CFD simulations are too computationally expensive to run continuously at runtime. DCIMate employs a surrogate model trained with machine learning that learns airflow behavior from both physical simulations and real-world data.
The result is near real-time estimation of the thermal field while maintaining good accuracy with respect to the physical model. The surrogate is periodically updated using new operational datasets.
The thermal model is integrated with a workload scheduling pipeline that jointly optimizes IT systems and cooling infrastructure. The system evaluates the thermal impact of different workload distributions via the digital twin and selects the configuration that minimizes total energy consumption, cooling utilization, and thermal hotspots.
Unlike traditional DCIM solutions, DCIMate simultaneously considers server workload, rack temperatures, airflow distribution, and cooling efficiency, enabling joint optimization that was previously not available.
DCIMate operates through a continuous processing pipeline. Sensor data, temperature, airflow, energy logs, and server utilization, is normalized and fed into the digital twin. The surrogate model computes temperature distribution, predicted thermal evolution, and effects of potential workload redistributions within seconds.
The scheduling algorithm then evaluates multiple scenarios and selects the configuration that best balances energy efficiency, thermal stability, and SLA compliance. The system generates workload balancing recommendations, ventilation policies, and optimal cooling configurations. In advanced mode, it operates in closed-loop control, integrating directly with existing DCIM or BMS systems.
DCIMate includes an interactive 3D viewer based on Three.js that represents the data center in near real time, rack layout, room topology, airflow paths and particles, and temperature fields.
The interface facilitates understanding of thermal phenomena and communication of results to operators and non-technical stakeholders. Operators can observe real-time rack temperatures, airflow patterns, and the impact of optimization decisions, all in an intuitive 3D environment.
Each ESP32-based sensor node integrates a low-power microcontroller with Wi-Fi/Ethernet connectivity and hardware interfaces for sensors (I2C, SPI, GPIO). The firmware runs a continuous cycle of sensor data acquisition, local filtering and normalization, and transmission of processed data to the DCIMate platform.
The firmware includes a lightweight embedded agent inspired by zclaw, which exposes hardware functionality through high-level commands. The agent acts as a software interface between DCIMate and the physical sensors, enabling sensor querying, threshold configuration, and firmware updates.
DCIMate is designed for enterprise data centers, HPC infrastructures, edge data centers, and cloud and colocation facilities, wherever energy efficiency drives operational cost and sustainability.