# Isolated Forest-Based Prediction of Container Resource Load Extremes
###### Abstract  
Given the wide application of container technology, the accurate prediction of container CPU usage has become a core aspect of optimizing resource allocation and improving system performance. The high volatility of container CPU utilization, especially the uncertainty of extreme values of CPU utilization, is challenging to accurately predict, which affects the accuracy of the overall prediction model. To address this problem, a container CPU utilization prediction model, called ExtremoNet, which integrates the isolated forest algorithm, and classification sub-models are proposed. To ensure that the prediction model adequately takes into account critical information on the CPU utilization's extreme values, the isolated forest algorithm is introduced to compute these anomalous extreme values and integrate them as features into the training data. In order to improve the recognition accuracy of normal and extreme CPU utilization values, a classification sub-model is used. The experimental results show that, on the AliCloud dataset, the model has an R\({}^{2}\) of 96.51% and an MSE of 7.79. Compared with the single prediction models TCN, LSTM, and GRU, as well as the existing combination models CNN-BiGRU-Attention and CNN-LSTM, the model achieves average reductions in the MSE and MAE of about 38.26% and 23.12%, proving the effectiveness of the model at predicting container CPU utilization, and provides a more accurate basis for resource allocation decisions.  
cloud computing; CPU prediction; cloud platform resource allocation; cloud platform CPU load prediction; temporal convolutional network +
Footnote †: journal: Applied  
1
Footnote 1: _Applied_  
used for container CPU utilization prediction, including autoregressive moving average models (ARIIMAs), Bayesian models, and recurrent neural networks (RNNs); however, they still struggle to deal with the high volatility and rare extreme values in container data, such as container CPU utilization. The existing models often struggle to accurately capture and predict these critical extreme fluctuations, which affects the accuracy of the prediction results and the generalization ability of the model. In addition, maintaining computational efficiency and adapting to variable load characteristics are also challenges hindering existing methods [7]. Therefore, there is an urgent need to further optimize the existing models or explore new strategies to enhance the prediction accuracy.  
In this paper, we propose a CPU utilization prediction model, named ExtremoNet, which incorporates isolated forest and classification sub-models to address the extreme value problem in container CPU utilization prediction. The model accurately identifies CPU utilization extreme values by introducing the isolated forest algorithm, and incorporates their abnormal scores as important features in the prediction framework, effectively enhancing the model's responsiveness to extreme situations. Combined with the classification sub-model, it further improves the accuracy of distinguishing between normal CPU utilization and extreme CPU utilization.  
This paper is organized as follows: Section 2 introduces the current state of the research on CPU load prediction; Section 3 introduces the ExtremoNet model structure; Section 4 describes the experimental process and related analyses; Section 5 summarizes the paper and proposes the next research direction.