Lack of real-time, granular, and vendor-agnostic visibility

Lack pattern-recognition in high-dimension data

Lack of end-to-end automated controls

How It Works


We solve the problem with a real-time software application that processes chip-level dynamic thermal data for granular, low-latency, and power-efficient cooling controls.



Digital Twin for Asset Monitoring / Maintenance

Digital twin gets real-time, granular, and vendor agnostic visibility into a data center. It processes the data for real-time predictive manitenance recommendations for better asset performance.

Digital Assistant for Continuous Cooling Optimization

Our AI-enabled Digital Assistant maintains optimal temperatures for your complex and one-of-a-kind data center.

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Case Study

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For the thermal management of a dense and virtualized data center hosting equipment from different vendors, a granular and vendor-agnostic visibility is warranted. With increasingly low-latency IT demand, a real-time visibility is also of importance.
In the absence of an appropriate pattern recognition algorithm, granular and real-time data is just computing burden. To fully utilize it, it is important to be supported by an efficient pattern-recognition algorithm.
Data center thermal management cuts through the entire stack starting from the electronic chip to the building chiller. An coordinated and concurrent operation requires end-to-end automated cooling controls.
This strategy ensures a proper alignment between cooling supply and demand, and provides fine-grained visibility into the thermal health of heterogeneous IT assets. Therefore, we can safely raise IT component temperatures by raising HVAC set points and reduce cooling bills without causing thermal hazards like hot spots.
Enterprise-grade servers supporting AI workloads are at least few orders of magnitude more expensive than the traditional volume servers. The equipment failure cost is too high to be ignored.
Typical lifetime of an IT equipment is 6-18 months, while that for a facilities equipment such as a Liebert cooling systems 15-20 years. The deployment times are also very different. Furthermore, the typical time-scale for data center facilities operations is in order of few minutes, while that for IT systems is few minutes. Therefore, matching data center facilities to its IT demand is very difficult.
Following Moore law’s, the number of transistors in electronic circuit is rapidly increasing at 1.3x annual rate. Moreover, virtualization of computing resources made data center workload rapidly non-uniform. This two factors lead to a complex data center operation.
Data centers are increasingly supporting application ecosystem such as video streaming, high-frequency merchandise, and real-time image recognition. This new generation applications require fast and high-throughput computation.
A typical data center hosts a multitude of IT, power, and cooling systems with varied response characteristics. This makes data center thermal profile non-uniform and complex. In addition to that, turbulent air cooling adds to the data center thermal profile to an extent that it become non-linear. Therefore, it is literally impossible to build a traditional formula-based mathematical modeling for data center thermal evolution. It necessitates a data-driven real-time framework for data center thermal modeling and prediction.
As much as 50% of data center downtimes happen due to thermal issues. AdeptDC’s data-informed predictive maintenance and automated cooling optimization reduces the risks of these downtimes.
Most of the hardware failures occur due to temperature, voltage, power, or humidity related issues. AdetpDC’s solution reduces the hardware mortality rates with its granular and real-time monitoring and end-to-end feedback control solution.
With data center cooling demand being difficult to predict, data center managers are forced to over-compensate their cooling capacity. AdeptDC’s end-to-end automated cooling optimization removes guessworks from the cooling controls, making it more efficient.
Although most of the data center managers are trained IT professionals, a significant part of their work hours are spent on calibrating cooling system and fixing thermal issues. AdeptDC’s predictive maintenance application intends to save time for IT professionals so that they can focus more on their core jobs.
Data center maintenance could be an intractable problem with unpredictable nature of data center operations and complex thermal response patterns. AdeptDC aims to removes this uncertainty and provides data center managers with much needed peace of mind.