# Isolated Forest-Based Prediction of Container Resource Load Extremes
## 4 Experimental Results and Analysis
### Data Processing  
Prior to further data processing, this study conducted a thorough quality assessment of the original AliCloud dataset to identify and correct potential data issues. Upon close inspection, the dataset was found to have missing data. To address the missing data and other problems in the dataset, we performed the following steps:  
(1) Missing value treatment: To ensure data integrity, the Lagrange interpolation [25] was used in this study to supplement the missing values. The Lagrange interpolation utilizes all known data points to construct the interpolation polynomial, ensuring that the polynomial passes through every known data point. This global property makes it possible to capture the overall trend of the data series, not just the localized changes.  
(2) Normalization: For preprocessing, MinMaxScaler was used to perform the feature normalization to eliminate the influence of the magnitude between different features in the  
Figure 3: Frequency distribution of CPU utilization in AliCloud datasets.  
Figure 2: AliCloud dataset CPU utilization.  
data and to improve the model training effect. MinMaxScaler can scale the feature values to a specified range, such as between 0 and 1, and its formula is expressed as  
\[X_{scaled}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{9}\]  
where \(X\) represents the input feature vector and \(X_{min}\) and \(X_{max}\) represent the minimum and maximum values in the feature vector, respectively. After MinMaxScaler processing, the data features will be in the range from 0 to 1, which is conducive to model training and optimization.  
(3) Extreme value determination: In order to determine whether a data point is an extreme value when training the model, the isolated forest algorithm is used to determine data points belonging to extreme values.  
Figure 4 shows the outlier detection results in the CPU utilization data using the isolated forest algorithm. Each point in the figure represents an observation in the dataset, where the horizontal coordinate represents the index of the data point, and the vertical coordinate represents the corresponding CPU utilization rate.  
The blue points in Figure 4 represent data points recognized as normal by the isolated forest algorithm. The CPU usage of these points is similar to the majority of the data points, indicating that their behavior is consistent with the overall distribution pattern of the dataset. The red points represent data points that are recognized as abnormal by the isolated forest algorithm. They are significantly different from the other data points in terms of CPU utilization and are therefore considered extreme values by the algorithm. These extreme values may indicate unusual or atypical behavior in the data.  
(4) Feature Selection: In order to effectively select the features to be used for predicting the target variable cpu_usage, this paper uses the Pearson correlation coefficient. This method identifies the features that have the most influence on the prediction results by calculating the degree of linear correlation between each feature and the target variable.  
Figure 5 shows the correlation coefficients of each feature with CPU_USAGE, and the importance of each feature is assessed based on the magnitude of the absolute value of these coefficients. Features with high correlation coefficients indicate a strong linear association with the target variable. Using this method, the most useful features for predicting CPU_USAGE are filtered from multiple features. From the above results, we can see that mem_gps has a low correlation coefficient, so we remove the mem_gps features from the data and all other features can be used for CPU utilization prediction.  
Figure 4: Detection of CPU utilization outliers based on the isolated forest algorithm.