Optimizing cloud resource allocation with an integrated XGBoost–RFOA model

Cloud computing has revolutionized the management of computational resources by providing scalable and on-demand services. Due to ever-changing workloads, budgetary restrictions, and the need to optimize performance, effective resource allocation continues to be a major obstacle. When trying to strike a balance between efficiency, fairness, and real-time adaptation, traditional static and dynamic allocation systems may be somewhat challenging. This work offers a state-of- the-art cloud resource allocation technique based on machine learning combined with metaheuristic optimization that enhances performance. More precisely, the study uses Extreme Gradient Boosting (XGBoost) in real-time along with the Red Fox Optimization Algorithm (RFOA) to maximize resource allocation. The proposed approach is tested against both Genetic Algorithm (GA) enhanced variants of XGBoost and the baseline. In terms of expected accuracy and resource economy, the experimental results reveal that the proposed model is superior to both substitutes. It gets a mean absolute error (MAE) of 21.23, a mean squared error (MSE) of 634.86, and an R-squared value of 0.009 after all around. The model uses RFOA to efficiently change hyperparameters to get ideal resource provisioning with low computational cost. This paper expands the subject of cloud resource management by demonstrating how successful optimization depending on metaheuristics is in machine learning. The data show that when complex algorithms are coupled with predictive analytics, cloud services are more dependable and reasonably priced. Future research could investigate hybrid models including deep learning approaches to maximize even more.