Data center IoT-based thermal optimization
The Internet of Things (IoT) is gaining traction across many industries including data centers. vXchnge, a provider of carrier-neutral colocation services, actively leverages innovative solutions and engineering best practices to achieve efficient and sustainable operations. Within that focus, vXchnge and Vigilent, a provider of dynamic cooling management systems, together engaged in a pilot project at vXchnge’s Chappaqua, NY, facility to improve thermal management and reduce energy spend. This case study describes how an IoT approach was used to manage the existing, multi-vendor cooling infrastructure within the North American colocation data center and highlights the results.
Data centers across the globe are struggling to free up resources to meet client cooling demands. In order to do this, more control and access to data for optimization of airflow is needed. Currently, legacy design standards provide more cooling than needed and airflow complexity and IT variability make it difficult to manually optimize with dynamic changes. This results in wasted energy, lost capacity, and hidden thermal risks. The challenge was to improve efficiency in legacy data centers with mixed, multi-generational equipment and to optimize operations through data-driven insights tied to a data center infrastructure management (DCIM) system. vXchnge’s Chappaqua, NY, facility was selected for the pilot as it houses legacy and varied vendor equipment.
The main goal was to optimize operations. To achieve the overall goal, a smaller subset of goals was established. These include: adding a layer of intelligence and automated control to reduce manual intervention, enhancing the ability to deliver a guaranteed 100% uptime for SLAs, achieving quantifiable efficiency gains and an attractive ROI, and providing real-time visibility throughout the cooling infrastructure.
vXchnge and Vigilent deployed an IoT-based approach to automatically optimize cooling for energy reduction and improved thermal management. This solution was selected ultimately to improve cooling capabilities within the data center. Vigilent has a solution that utilizes machine learning, allowing the system to continuously get smarter. The system exceeded expectations with having a “guard mode” to activate cooling in the event of any system failures or when temperatures exceed a certain threshold. The “guard mode” brings added protection to the data center until the mechanical system returns to normal operation, all driven through the automation and learning algorithms.
The IoT system is composed of a wireless mesh network of hundreds of sensors and controllers driven by machine learning software. The Vigilent system leverages the sensor network automatically creating a real-time model of the facility’s thermal environment by mapping airflow and determining the precise cooling influence of every unit, both individually and collectively, at every spot across the data center. The system then takes dynamic control of the cooling units — turning them on and off, and ramping fan speeds up and down — to meet pre-specified temperature SLAs in the most efficient manner possible.
Automatic thermal optimization through predictive control measures heat load and cooling equipment efficiently and models cooling airflow influence. Using machine learning algorithms, the IoT-based system learns the effects of control actions and manipulates the cooling equipment by itself without staff intervention.
TESTING AND RESULTS
The testing at the Chappaqua facility was conducted over a six-month timeframe and led to actual savings exceeding the projected numbers in each site, as well as continuous savings, even as the environments change, and with intelligent cooling control with equipment of mixed brands and different generations of technology. Specific insights into the thermal management systems were uncovered and include exactly where cooling was delivered across the white space, where new IT capacity could be deployed, where additional cooling may be needed to provide sufficient capacity and redundancy, and which cooling units may be underperforming.
The testing also delivered some unexpected advantages in the overall thermal management system, dynamically matching cooling capacity to the actual load eliminating the need for manual tuning and adjustments. Using analytics helps to more easily identify potential issues, automatically resolving hot and cold spots, directly impacting facility staff productivity. With automatic closed loop thermal control, staff is no longer manually managing the thermal environment, giving them more time to engage in proactive management and customer service.
Furthermore, non-energy related benefits were also discovered. These manifested into easier capacity planning, increased visibility, and a more stable environment. Because of the Chappaqua facility’s success, the IoT-based approach for thermal management optimization was implemented in two additional vXchnge data center facilities with similar results.
Since its founding in 2008, Involta LLC, Cedar Rapids, IA, has continually looked for the most efficient and effective method of cooling for their network of data centers.
For example, its Marion, IA, data center was built with conventional rooftop air handlers and metal ductwork that dispersed air from ceiling registers. A retrofit changed to metal drops supplying traditional porous fabric ductwork typically seen in open architectural applications such as retail stores and gymnasiums.
A second retrofit was the foundation of methods Involta is using in new construction and retrofits today, such as VFDs and data center-specific DataSox air dispersion Involta collaboratively designed with fabric duct manufacturer DuctSox, Turbulent, IA. The newest location, Involta Northpointe, Freeport, PA, uses DataSox and VFDs on CRACs with free cooling options and micro-channel condensers. The CRACs are connected with separate supply air and return air plenums.
The result is a 1.3 PUE, which puts the Northpointe location in the top five percentile of efficient multi-tenant, colocation data centers nationwide.