# Isolated Forest-Based Prediction of Container Resource Load Extremes
## 4 Experimental Results and Analysis
### Comparison Experiment  
In the experiments, the time step is set to [2; 6; 12; 24] time units, i.e., cloud load data from the past [2; 6; 12; 24] time units are used as input features. Based on these input features, the model will predict the cloud loading scenarios, while three evaluation metrics, namely, the MSE, MAE, as well as R-squared values, are used to evaluate the model's performance. Table 2 and Figure 6 clearly show the performance of each model on the AliCloud test dataset. ExtremoNet performs best compared to a single model. The experimental results show that the ExtremoNet model achieves the best performance on the AliCloud dataset. The MSE of the model is 7.79 and the R\({}^{2}\) is 96.51%. In this paper, the proposed model is compared with the GRU-LSTM model proposed in [22], the pCNN-LSTM model proposed in [13], and the CNN-BiGRU-Attention model proposed in [14]. The prediction results are shown in Figure 7, and the results of the evaluation metrics are shown in Table 3.  
On the AliCloud dataset, the ExtremoNet model significantly outperforms the other models. For a 12-step length, the MSE of ExtremoNet is 7.79, while the MSEs of GRU-LSTM, pCNN-LSTM, and CNN-BiGRU-Attention are 14.82, 11.85, and 15.54, respectively. Compared to GRU-LSTM, pCNN-LSTM, and CNN-BiGRU-Attention, the MSE of ExtremoNet is reduced by 47.44%, 34.26%, and 49.87%. Similarly, ExtremoNet performs better in the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Step** & **2** & **6** & **12** & **24** \\ \hline \multirow{3}{*}{ExtremoNet} & MSE & 9.23 & 8.62 & 7.79 & 8.53 \\  & MAE & 1.53 & 1.66 & 1.44 & 1.48 \\  & R\({}^{2}\)(\%) & 94.10 & 94.75 & 96.51 & 95.89 \\  & MSE & 22.21 & 19.17 & 16.07 & 15.13 \\ ARIMA & MAE & 2.88 & 2.40 & 2.31 & 2.19 \\  & R\({}^{2}\)(\%) & 84.93 & 85.01 & 87.82 & 88.01 \\  & MSE & 20.15 & 13.20 & 12.94 & 12.46 \\ TCN & MAE & 2.70 & 1.91 & 1.88 & 1.85 \\  & R\({}^{2}\)(\%) & 85.33 & 90.43 & 90.64 & 90.83 \\  & MSE & 19.38 & 14.15 & 13.93 & 13.17 \\ LSTM & MAE & 2.49 & 2.07 & 2.21 & 2.17 \\  & R\({}^{2}\)(\%) & 85.89 & 89.74 & 89.92 & 90.21 \\  & MSE & 16.32 & 16.44 & 13.79 & 12.57 \\ GRU & MAE & 2.37 & 2.2 & 1.94 & 1.84 \\  & R\({}^{2}\)(\%) & 88.12 & 88.11 & 90.00 & 90.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for Alibaba Cloud dataset. Figure 5: Correlation coefficients between different features in the dataset and the target feature. MAE metric, which is reduced by 23.40%, 24.21%, and 21.74% compared to GRU-LSTM, pCNN-LSTM, and CNN-BiGRU-Attention, respectively. model utilizes the isolated forest algorithm to identify and quantify extreme values and integrates the classification sub-model to further improve the model's ability to distinguish between normal and extreme fluctuations. Experiments conducted on the AliCloud dataset confirmed the effectiveness of our model, and it was compared with existing methods. The results show that ExtremoNet has significant advantages in its ability to recognize and predict extreme fluctuations, providing reliable support for container resource scheduling and decision making. It successfully bridges the gap of a single model in dealing with anomalous data, demonstrating efficiency and reliability in real-world applications. Despite ExtremoNet's excellent prediction accuracy, there are still some challenges to be overcome. Due to the highly integrated structure of the model, its computational requirements are high, which may affect the responsiveness of the model in real-time prediction scenarios. In future work, we plan to explore ways to classify prediction intervals to further refine the prediction accuracy, and to consider more advanced techniques to reduce the computational burden of the model to ensure that it maintains its efficiency and accuracy in rapidly changing cloud computing environments. Conceptualization, C.W. ; methodology, Z.W. ; validation, Z.W. ; investigation, Z.W. ; data curation, Z.W. ; writing--original draft preparation, Z.W.