Deep Learning-Based Prediction of SaaS Instance Configurations for Cloud Resource Optimization

There are numerous concerns related to the extensive use of cloud computing, including performance issues with communication- and computation-intensive services on virtual cloud resources. The present study explores the utilization of a Deep Neural Network (DNN) model to predict and optimize SaaS instance configurations on the basis of analyzing a dataset on Kaggle, which harbors Amazon AWS SaaS Sales information. After the data preparation process, such as filling in missing values, handling outliers, and normalizing features, to guarantee the validity of the results, the data is separated into training, validation, and test sets. The DNN model is then trained and tested using a number of different performance measures: accuracy, precision, recall, F-measure, and loss. The model is found to be not only excellent but also very accurate, reaching an accuracy percentage of 96.33%, and a strong balance between precision and recall is maintained. The learning curves suggest that overfitting was not a factor during the training process, which was smooth and stable, while the confusion matrix supports the model's reliability. The DNN has consistently outperformed other common methods, including KNN, LGBM, ARIMA, AdaPCA, and SV, in different datasets. To sum up, the insights obtained from this study point to the robustness and the capability of the model in processing SaaS sales data as well as in optimizing cloud resource configurations.