Optimising Resource Sharing Algorithms for Efficient Cloud Computing

Cloud computing dramatically enhances digital resource delivery as workload patterns keep changing and hinder effective resource sharing. This research develops an efficient resource management solution using machine learning algorithms, including a random forest classifier, a gradient boosting classifier, and a support vector classifier. This study analyses CPU usage, memory usage, and network traffic in the Cloud Computing Performance Metrics dataset to create precise demand forecasts. Our model results improved after applying data preparation steps to normalize and engineer the features. The new system dynamically allocates resources based on predictive models, helping the platform reduce cost and improve delivery speed. Our test results show that asset management performs better than fixed resource distribution and load-spreading systems, especially in demanding cloud environments.