Intelligent Predictive System of Cloud Resource Utilization Forecasting with Advanced Deep Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P115Keywords:
Cloud Computing, Resource Utilization Prediction, CPU Usage Forecasting, Machine Learning, Deep Learning, Cloud Resource ManagementAbstract
Time series forecasting is important in cloud data centers to efficiently allocate resources in the form of cloud resources, since forecasting demand allows effective use of computing resources and reduces costs. Traditional methods based on conventional machine learning or statistical analysis often fall short at capturing complex temporal patterns, leading to low prediction accuracy and inefficient resource use. Three models are proposed in this research. Using actual Microsoft Azure trace data, CNN, GRU, and a hybrid CNN-GRU are used to forecast resource usage. The experimental results show that the CNN-GRU model achieves the highest performance, with the lowest error rates (MSE: 0.0002, MAE: 0.0136, RMSE: 0.0164) and the maximum R2 of 98.23%, compared to the single CNN (R2: 94.59%) and GRU (R2: 97.45%). The proposed models show much better predictive accuracy than traditional forecasting methods such as CP-SAE and LSTM. The findings validate the importance of combining spatial and temporal learning features for powerful forecasting of cloud resources. The paper provides an actionable model for proactive resource distribution and lays the groundwork for future developments based on multi-source real-time data and an advanced architecture to further increase prediction accuracy in dynamic cloud-based environments.
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