## 4 Results  
After 7 months of the data center's construction, we assessed the solution's efficiency regarding the servers' cooling temperature control since the load to be cooled increased with the entry of more than 80 pieces of equipment. In this way, we compared the average values of inlet air temperatures recorded by servers installed in our new data center. These average values are an essential measurement parameter of the efficient cooling equipment's study within a data center, and this efficiency directly impacts energy consumption. [PERSON] [58] states that the temperature of the servers' entrance is one of the most critical points in the operation of a data center. Therefore, controlling that temperature is critical to any data center's efficient performance. Figure 4: Cold aisle containment, by adding doors and a closed roof to the aisles in such a way as to contain and concentrate the cold airflow in the front area of the racks. We collected temperature data of eight high-scale processing servers, which by the nature of their activities, tend to induce high heat production (all servers are equipped with three NVIDIA Tesla V100 GPUs for data science research). These data are automatically recorded by sensors located on the motherboard of these servers and stored in an internal log. Hence, there is no interference from the operating system or any other software running on the same server. We chose these high-scale processing servers because they are positioned at the top of the racks, approximately 178 cm away from the cold air outlet coming from the raised floor. Bearing in mind that, compared to other equipment located in lower positions of the rack, these servers tend to draw less cold air and, consequently, register higher inlet temperatures than the others. Once the sampling elements have been defined, the set of input temperature data collected from each server is consolidated in such a way as to calculate the individual inlet temperature averages of these eight devices, considering two specific periods:  
* April 2018-April 2019--Period before moving to the new data center, from April 2018, when these servers were installed in the old data center, until April 2019, when we shut down these eight servers to change the building;
* May 2019-January 2020--Period after the change of building, from May 2019, when we started the initialization of the eight servers already in the new data center, until January 2020, when we set the cutoff point of this study. Table 2 shows the data extracted from the eight servers, taking into account the two periods mentioned above. We can see the temperature reduction through the analysis of the column \"Average Temperature Difference (\({}^{\circ}\)C)\", which is a result of the comparison of the average temperature collected before and after the move to the new data center. For example, we have the GPU Server 06, which in the old environment had an inlet temperature of 28.18 \({}^{\circ}\)C, and in the new environment, it had 15.26 \({}^{\circ}\)C. For this server, there was an environmental heat suppression of approximately 46%, thus minimizing the possibility of interference from high temperatures that could be harmful to its processing performance or its general functioning. Considering the temperature data extracted from the GPUs servers during the two periods as statistically valid samples, which represent the inlet temperature of all other servers of the data center, it is possible to verify that there was an equivalent reduction of temperature for all equipment in the new data center of approximately 41%. This implies a reduction in the cost of current cooling equipment and the possibility of adding more equipment to be refrigerated. The temperature reduction in the new data center was achieved by reusing the same three CRACs (model Stultz ASD 1072 A, 30 TR) from the old data center, despite the