# Intelligent Monitoring of Data Center Physical Infrastructure
## 3 Results
### Experimental Results of a Reduced Number of Alarms by RCM
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{-} & \multirow{2}{*}{DEG} & **Psc** & **Psc** & **Psc** & **Pdu** & **Pdu** & **UPS** & **UPS** & **Cch** & **Cch** & **Psc** & **Psc** \\ \cline{2-13}  & & **1** & **2** & **3** & **1** & **2** & **A** & **B** & **A** & **B** & **A** & **B** \\ \hline
**Events** & 3 & 18 & 23 & 28 & 27 & 24 & 5 & 3 & 12 & 12 & 9 & 9 \\
**Events after** & 2 & 16 & 20 & 24 & 23 & 20 & 2 & 1 & 9 & 8 & 9 & 9 \\
**Events** & 2 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 \\
**Alarms** & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
**Alerts** & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the alert/event number (with root cause method (RCM))—data center power supply level. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{-} & \multirow{2}{*}{DEG} & **Psc** & **Psc** & **Psc** & **Pdu** & **Pdu** & **UPS** & **Cch** & **Cch** & **Psc** & **Psc** \\ \cline{2-13}  & & **1** & **2** & **3** & **1** & **2** & **A** & **B** & **A** & **B** & **A** & **B** \\ \hline
**Events** & 3 & 18 & 23 & 28 & 27 & 24 & 5 & 3 & 12 & 12 & 9 & 9 \\
**Events after filtering** & 2 & 16 & 20 & 24 & 23 & 20 & 2 & 1 & 9 & 8 & 9 & 9 \\
**Alarms** & 2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the alert/event number (without root cause method (RCM))—data center power supply level. For example, in Psc 1, 2, and 3, the system cabinets are all directly connected to power switches. They all have the same parental internal hub and the same power supply group (UPS A, B) (Figure 3). The alarm events of these system cabinets were merged and therefore reduced to the same internal node. Therefore, due to the RCM, the alerts were carried out for the following:  
* The diesel generator, which is also the main cause and one redundant power option;
* UPS A and UPS B, which were identified as a root cause because of the redundancy option;
* Cch A and Cch B, which have the same power source as the main network power supply. From the number of events, it was calculated that the number of saved alarms/alerts decreased to 38%, which means that the average number of all alarms decreased by 62% (Table 4). Another experiment was performed on data stored in the history on messages sent to administrators. These messages contained all the information about events that had happened in the past. The event data that were stored were compared to the currently detected causes. The messages were gathered by blocks (system functional parts) and by data type in the CODESYS software. Event declarations can be sorted by individual blocks. The connections to nodes to which an event is related can be determined in the list and during event identification. The event origin (device, node, type of event) can be determined for event variables. An example of the table with event declarations and alarm statuses for the cooling cabinets is shown in Table 5 below. Table 6 shows an example of alarm activation areas with variables for the border regions for the cooling cabinet temperature measurement. With the RCM running on the stored data, the number of events was decreased by -50% for the period from 2016 to 2019 (Table 7). Table 7, a comparison of the total numbers of all events between 2016 and 2019, shows that when using the RCA method, the number of detected real alarms increased with increasing number of alarms (events). There were no data center downtime periods during the event collection period.