## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Equipment**} & \multicolumn{2}{c}{**Average Inlet Temperature**} & \multicolumn{2}{c}{**Average Inlet Temperature**} & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Temperature** \\ **Difference (\({}^{\circ}\)C)** \\ \end{tabular} } & \multicolumn{2}{c}{**Percentage of**} \\  & **(\({}^{\circ}\)C)—April 18–April 19 (SD)** & **(\({}^{\circ}\)C)—May 19–January 20 (SD)** & **Difference (\({}^{\circ}\)C)** & **Reduction** \\ \hline GPU Server 01 & 25.76 (\(\pm\)3.42) & 15.35 (\(\pm\)1.42) & 10.41 & 40\% \\ GPU Server 02 & 22.60 (\(\pm\)2.00) & 15.27 (\(\pm\)1.41) & 7.33 & 32\% \\ GPU Server 03 & 26.83 (\(\pm\)3.41) & 16.31 (\(\pm\)1.43) & 10.53 & 39\% \\ GPU Server 04 & 25.55 (\(\pm\)3.80) & 15.21 (\(\pm\)1.37) & 10.34 & 40\% \\ GPU Server 05 & 22.77 (\(\pm\)1.26) & 15.31 (\(\pm\)1.47) & 7.47 & 33\% \\ GPU Server 06 & 28.18 (\(\pm\)1.94) & 15.26 (\(\pm\)1.41) & 12.91 & 46\% \\ GPU Server 07 & 28.04 (\(\pm\)1.26) & 15.28 (\(\pm\)1.49) & 12.76 & 46\% \\ GPU Server 08 & 27.34 (\(\pm\)1.97) & 15.21 (\(\pm\)1.40) & 12.13 & 44\% \\ \hline Average & 25.88 & 15.40 & 10.48 & 41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Extracted inlet temperatures and percentage of reduction of temperatures per server for the two analyzed periods: before moving to the new data center (April 18–April 19), and after (May 19–January 20). addition of 36 more pieces of network equipment (routers and switches) and 44 new servers, added to the 346 pieces old equipment (servers, storage devices, network assets, and others). In the old data center, where less equipment was cooled, we used the theee CRACs simultaneously and uninterruptedly throughout the week, with no redundancy if one of the units failed, which was a problem. In contrast, after implementing the improvements in the new data center, we have worked with redundancy, rotating the CRACs weekly, so that we have one of the units in standby mode and the other two units operating simultaneously. In order to evaluate the data center energy efficiency, we applied the Power Usage Effectiveness (_PUE_) value [59]. The (_PUE_) is defined by [59] as the total power supplied to the data center divided by the power consumed by IT equipment, defined in Equation (1). The target value is 1.0 since the lower the value (closer to 1), the better the data center's energy efficiency index [52; 60]  
\[PUE=\frac{\text{Total data center power consumption}}{\text{IT equipment power consumption}} \tag{1}\]  
We measured 473,832 kWh of power consumed by IT equipment and the value of 532,218 of total electrical power supplied to the data center. In this way, the value of the (_PUE_) obtained was: 1.123. Furthermore, we calculated the Data Center infrastructure Efficiency (_DCiE_) defined in Equation (2) and obtained 88% efficiency, i.e., the reciprocal of (_PUE_). \[DCiE=\frac{1}{PUE} \tag{2}\]  
Compared to other companies, our results demonstrated competitive energy efficiency [60] when conducting a comparative study of (_PUE_) in data centers of different companies, e.g., Google (1.12), Facebook (1.08), Microsoft (1.12), eBay (1.45), Yahoo (1.08), HP (1.19), and others. According to [60], we would be among the 2% of the best energy-efficient data centers. It indicates that with the reuse of legacy systems, layout changes, and cooling improvements, we beat the results of other similar big companies.