Enhancing Cloud Resource Management Through Machine Learning-Based Utilization Prediction

Cloud resource management is important in the provision of efficiency in the allocation, scalability, and performance of virtualized computing environments. The ability to predict resource consumption is a very important and highly effective machine learning method that supports cloud operations in a perfect way by predicting future resource usage. An improved method for analyzing Microsoft Azure Trace data for forecasting is proposed in this research called BiLSTM-GRU, a deep learning architecture that combines Bidirectional Long Short-Term Memory with Gated Recurrent Units. The process for producing high-quality time-series inputs includes thoroughly cleaning the data, reducing noise, engineering features, and normalizing. BiLSTM captures temporal dependencies in both directions while GRU sequentially learns efficiently via gated operations, thus enabling both short- and long-term workload trend capture by the model. The LiR model outperforms the other linear resource trend models in terms of R² and RMSE (0.0169) according to the experimental data, while the BiLSTM-GRU model boasts the greatest prediction accuracy (0.9832%). The hybrid model's detection of the complex temporal variations in cloud workloads is further validated by a comparative analysis against the current NN, BP, and ARIMA models. The BiLSTM-GRU model is not only resourceful but also stable and offers correct CPU usage predictions, which enhance the efficiency of the cloud.