## 7 Results and Discussion
### LSTM-Based Scheduling  
In this section, the importance of CPU-utilization prediction is explored in the automated-recovery process. First, an experiment selecting the target cluster for restoration based on CPU-utilization prediction by using LSTM is compared with an experiment randomly selecting the target cluster. In each experiment, restoration operations were conducted 10 times on five clusters, starting from the same initial state, and the variations in CPU utilization in each cluster were analyzed. According to [PERSON] et al. [29], when CPU utilization exceeds 80%, the occurrence of system performance degradation is highly possible. Thus, maintaining a stable level of CPU utilization in the cluster is important. This study thoroughly analyzed the results of both experiments, presenting a comparative analysis. Table 2 shows the results of randomly selecting a target cluster among five clusters and performing restoration 10 times. The red text in the table shows which cluster the restoration at that point is scheduled for. The first restoration occurred in Cluster 5, demonstrating the highest initial CPU utilization, followed by several other restorations. After 10 restoration operations, Clusters 4 and 5 reached 85% CPU utilization, indicating a high probability of performance degradation. Moreover, CPU utilization across clusters demonstrates significant variation. Table 3 presents the results of selecting the target cluster for restoration based on CPU-utilization prediction with LSTM and conducting 10 restorations. The red text in the table shows which cluster the restoration at that point is scheduled for. The first restoration began in Cluster 1, which has the lowest initial CPU-utilization rate, and subsequent restoration operations targeted clusters with the lowest rate of CPU utilization at each point. After completing 10 restoration operations, none of the clusters exceeded 80% CPU utilization. Moreover, all clusters maintained a stable state, with CPU utilization between 55% and 70%. The results of these two experiments demonstrate the importance of scheduling based on CPU-utilization prediction. As shown in Table 2, in Experiment 1, restoration was started in Cluster 5, which had high initial CPU utilization, leading to performance degradation. Eventually, CPU utilization in Clusters 4 and 5 reached 85%, increasing the risk of performance degradation and showing an imbalance in utilization among clusters. In contrast, in Experiment 2, (Table 3) restoration was started in clusters with lower CPU utilization and LSTM for prediction, which helped to maintain system balance. All clusters \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Restoration Count** & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & **Cluster 5** \\ \hline Initial State & 35\% & 40\% & 50\% & 60\% & 70\% \\ \hline
1 & 35\% & 40\% & 50\% & 60\% & 75\% \\ \hline
2 & 40\% & 40\% & 50\% & 60\% & 75\% \\ \hline
3 & 40\% & 40\% & 50\% & 65\% & 75\% \\ \hline
4 & 40\% & 40\% & 50\% & 70\% & 75\% \\ \hline
5 & 40\% & 40\% & 50\% & 70\% & 80\% \\ \hline
6 & 40\% & 40\% & 50\% & 70\% & 85\% \\ \hline
7 & 40\% & 40\% & 55\% & 70\% & 85\% \\ \hline
8 & 40\% & 40\% & 55\% & 75\% & 85\% \\ \hline
9 & 40\% & 40\% & 55\% & 80\% & 85\% \\ \hline
10 & 40\% & 40\% & 55\% & 85\% & 85\% \\ \hline \hline Clusters with CPU utilization under 80\% & O & O & O & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of randomly scheduling clusters. maintained a stable state, not exceeding 80% CPU utilization, and a utilization rate between 55% and 70% indicated efficient resource usage. Additionally, we compared five prediction methods: Random Selection, Lowest (5 m ahead), Lowest (10 m ahead), LSTM (5 m ahead), and LSTM (10 m ahead). Table 4 presents the RMSE for these five methods. Random Selection shows the results of randomly selecting a cluster from the five clusters.