Abstract: Systems, methods, and apparatuses are described for automatically generating and simulating cloud infrastructure topologies. A computing device may provide a user interface displaying a cloud infrastructure topology and may receive user input indicating modifications to that topology. The computing device may generate simulation data by determining estimated resources used by each element of the modified version of the cloud infrastructure topology during simulation of the modified version of the cloud infrastructure topology. Such simulation might be determined by simulating events, probabilistically, and/or through use of a trained machine learning model. The computing device may then identify recommended changes to the modified version of the cloud infrastructure topology based on that simulation data and cause output of those recommended changes. Such recommendations might relate to moving various elements to different cloud service providers to, for example, improve stability and/or lower costs.

Abstract: Methods, systems, and apparatuses are described herein for automatically recommending cloud server configuration changes. A machine learning model may be trained to output infrastructure modification recommendations based on a history of performance measurements of a server environment that executes one or more applications. Input data indicating a current configuration of the server environment may be provided to the trained machine learning model. In response, the trained machine learning model may output a recommended change to the server environment. Based on comparing the risk score to a threshold associated with the one or more applications, users might be provided with the option to implement the recommended change by modifying one or more operating parameters of one or more servers of the server environment. Additionally and/or alternatively, the recommended change may be automatically implemented.

Abstract: Systems, methods, and apparatuses are described for automatically generating and simulating cloud infrastructure topologies. A computing device may provide a user interface displaying a cloud infrastructure topology and may receive user input indicating modifications to that topology. The computing device may generate simulation data by determining estimated resources used by each element of the modified version of the cloud infrastructure topology during simulation of the modified version of the cloud infrastructure topology. Such simulation might be determined by simulating events, probabilistically, and/or through use of a trained machine learning model. The computing device may then identify recommended changes to the modified version of the cloud infrastructure topology based on that simulation data and cause output of those recommended changes. Such recommendations might relate to moving various elements to different cloud service providers to, for example, improve stability and/or lower costs.

Abstract: Methods, systems, and apparatuses are described herein for automatically recommending cloud server configuration changes. A machine learning model may be trained to output infrastructure modification recommendations based on a history of performance measurements of a server environment that executes one or more applications. Input data indicating a current configuration of the server environment may be provided to the trained machine learning model. In response, the trained machine learning model may output a recommended change to the server environment. Based on comparing the risk score to a threshold associated with the one or more applications, users might be provided with the option to implement the recommended change by modifying one or more operating parameters of one or more servers of the server environment. Additionally and/or alternatively, the recommended change may be automatically implemented.