Dynamic cloud resource allocation using machine learning

A method for automatically recommending cloud server configuration changes, the method comprising:

• receiving, by a computing device, training data comprising a history of performance measurements of a server environment that executes one or more applications;
• generating, by the computing device, a trained machine learning model to output infrastructure modification recommendations by training, based on the training data, a machine learning model implemented using an artificial neural network, wherein training the machine learning model comprises modifying one or more weights of the artificial neural network based on the training data;
• providing, by the computing device and to the trained machine learning model, input data comprising an indication of a current configuration of the server environment;
• receiving, by the computing device, as output from the trained machine learning model, and in response to the input data, an indication of a recommended change to the server environment, wherein the indication of the recommended change to the server environment comprises a risk score associated with the recommended change;
• based on comparing the risk score to a threshold associated with the one or more applications, causing output, by a second computing device, of a user interface element that provides an option to implement the recommended change to the server environment;
• and
• based on user input received in response to the user interface element, implementing, by the computing device, the recommended change by modifying one or more operating parameters of one or more servers of the server environment.

One or more non-transitory computer-readable media storing instructions configured to automatically recommend cloud server configuration changes, wherein the instructions, when executed by one or more processors of a computing device, cause the computing device to:

• receive training data comprising a history of performance measurements of a server environment that executes one or more applications;
• generate a trained machine learning model to output infrastructure modification recommendations by training, based on the training data, a machine learning model implemented using an artificial neural network, wherein training the machine learning model comprises modifying one or more weights of the artificial neural network based on the training data;
• provide, to the trained machine learning model, input data comprising an indication of a current configuration of the server environment;
• receive, as output from the trained machine learning model and in response to the input data, an indication of a recommended change to the server environment, wherein the indication of the recommended change to the server environment comprises a risk score associated with the recommended change;
• based on comparing the risk score to a threshold associated with the one or more applications, cause output, by a second computing device, of a user interface element that provides an option to implement the recommended change to the server environment;
• and
• based on user input received in response to the user interface element, implement the recommended change by modifying one or more operating parameters of one or more servers of the server environment.