## 7 Results and Discussion
### LSTM-Based Scheduling
Lowest (5 m ahead) involves selecting the cluster with the lowest CPU usage among the five and predicting the CPU usage for the next 5 min based on the current value. Similarly, Lowest (10 m ahead) predicts the CPU usage for the next 10 min using the same approach. LSTM (5 m ahead) utilizes 15 min of data from the five clusters to predict the CPU usage for the next 5 min, while LSTM (10 m ahead) uses 30 min of data to predict the CPU usage for the next 10 min. As shown in Table 4, LSTM demonstrates approximately 2.2% lower RMSE than Lowest. This comparison proves that prediction-based scheduling by using LSTM is crucial for resource management and performance degradation prevention in cloud environments. When the prediction model was used to determine the order of restoration operations, it enhanced the overall system performance, increased resource usage efficiency, and minimized the risk of potential performance degradation. LSTM is widely used for its strong ability to model long-term dependencies. However, due to its intrinsic recurrent approach, there is a tendency for computational and memory usage to increase as the sequence length becomes longer. This limitation can also be observed in Table 4. For example, the RMSE for a prediction made 5 min later using LSTM improved by about 50.8% compared to the RMSE for a prediction made 20 min later. Therefore, this constraint can lead to problems in large datasets or real-time applications. Moreover, the scalability issue of LSTM models is also worth noting in relation to parallelization. Due to its sequential nature, parallelizing LSTMs is generally challenging, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Random** & \begin{tabular}{c} **Lowest** \\ **(5 m ahead)** \\ \end{tabular} & \begin{tabular}{c} **Lowest** \\ **(10 m Ahead)** \\ \end{tabular} & \begin{tabular}{c} **Lowest** \\ **(20 m Ahead)** \\ \end{tabular} & \begin{tabular}{c} **LSTM** \\ **(5 m Ahead)** \\ \end{tabular} & \
\begin{tabular}{c} **LSTM** \\ **(10 m Ahead)** \\ \end{tabular} &
\begin{tabular}{c} **LSTM** \\ **(20 m Ahead)** \\ \end{tabular} \\ \hline RMSE & 3085.83 & 312.30 & 472.20 & 608.09 & 305.54 & 461.75 & 621.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Random Selection vs. Lowest Selection vs. LSTM-based Selection. \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 & 40\% & 40\% & 50\% & 60\% & 70\% \\ \hline
2 & 45\% & 40\% & 50\% & 60\% & 70\% \\ \hline
3 & 45\% & 45\% & 50\% & 60\% & 70\% \\ \hline
4 & 50\% & 45\% & 50\% & 60\% & 70\% \\ \hline
5 & 50\% & 50\% & 50\% & 60\% & 70\% \\ \hline
6 & 55\% & 50\% & 50\% & 60\% & 70\% \\ \hline
7 & 55\% & 55\% & 50\% & 60\% & 70\% \\ \hline
8 & 55\% & 55\% & 55\% & 60\% & 70\% \\ \hline
9 & 60\% & 55\% & 55\% & 60\% & 70\% \\ \hline
10 & 60\% & 60\% & 55\% & 60\% & 70\% \\ \hline Clusters with CPU utilization under 80\% & O & O & O & O & O \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of scheduling clusters based on CPU-usage prediction by using LSTM. which can slow down training speeds in large datasets. These limitations can restrict the practical applicability of LSTMs in real-world scenarios. Therefore, future research needs to consider the Transformer architecture. Transformers can effectively address long-term dependency issues using attention mechanisms and are efficient in parallel processing [30]. Transformer architectures for time-series data prediction have been recently studied and leveraging them could overcome the limitations of LSTMs [31; 32].