Machine learning (ML) in cloud architectures is used to manage powerful servers that run distributed systems over the internet. ML predicts the workload and traffic from cloud consumers and allocates resources according to the demand. ML in cloud architectures is there to improve performance and increase availability to manage cloud computing resources. The combination of ML and cloud architectures balances the workload and ensures reliability. This research discusses cloud architectures that use ML to run different algorithms to predict the improvement in the cloud architectures by using a cloud computing resource dataset. The dataset is used with different classifiers with the same ML framework that is discussed in this paper; the ML framework has a sequence to provide the steps of the model training and testing and uses different techniques and methods for the better performance of the cloud architectures. The researchers used various ML techniques to create a model for predicting the workload. To enhance the model’s performance and flexibility, we used a regression-based dataset that was recently updated, which was used with different ML approaches to predict better performance in the cloud architectures. By using the Generalized Linear Model, we achieved the highest performance. The R2 value refers to the goodness of the model and its performance. Using cloud datasets and machine learning with cloud architectures enhances performance using the different techniques in this paper, resulting in a more generalizable model with overfitting risk. This study focuses on refining the execution of cloud architectures with the help of ML.
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