Ensemble-Based Cloud Workload Prediction Using Recent AI and ML Methods for Optimized Resource Management & Scheduling

with the rising demand for efficient cloud computing and resource management, precise workload prediction has become vital. This paper explores altered methods used for workload predicting, from traditional methods to recent machine learning methods. We train models such as XGBoost, LightGBM, CatBoost, LSTM, and GRU, along with an ensemble method, to know their efficiency in practical cloud environments. The study uses the Alibaba Cluster 2017 dataset, focusing on batch (offline) workloads for well prediction precision. Numerous pre-processing methods, with outlier detection, normalization, and sequence creation, are applied to increase model performance. We associate the results of distinct models and ensemble methods using performance parameters like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that whereas deep learning models seizure sequential patterns, ensemble techniques deliver improved complete stability and correctness. This research shows the importance of merging multiple models to improve workload predicting and increase cloud resource consumption.