Abstract: Methods and systems for selecting, testing, and applying application configurations are presented. In one embodiment, a method is provided that includes executing an application according to a first configuration and measuring a first plurality of metrics. One or more changes to a plurality of configuration settings of the first configuration may be identified by a machine learning model to generate one or more new configurations. Among the one or more new configurations, a second configuration for future executions of the application may be selected based on the first plurality of metrics and a second plurality of metrics associated with an execution of the application.

Abstract: Methods and systems for selecting, testing, and applying application configurations are presented. In one embodiment, a method is provided that includes executing an application according to a first configuration and measuring a first plurality of metrics. A change to a setting of the first configuration may be identified by a machine learning model to generate a second configuration. The application may be executed according to the second configuration and a second plurality of metrics may be measured. A selected configuration for future executions of the application may be selected from among the first and second configurations based on the first plurality of metrics and the second plurality of metrics.

Abstract: Methods and systems for detecting and correcting inefficient application configurations are presented. In one embodiment, a method is provided that includes receiving, at a first computing environment, a configuration for and execution metrics of an application executed within a second computing environment; identifying, via a machine learning model, a corresponding preferred configuration and expected metrics associated with the corresponding preferred configuration; identifying a difference between at least one of the execution metrics and at least one of the expected metrics; and applying the corresponding preferred configuration to the application within the second computing environment.

Abstract: Methods and systems for detecting and responding to erroneous application configurations are presented. In one embodiment, a method is provided that includes receiving a configuration for an application and receiving execution metrics for the application. The configuration and the execution metrics may be compared to a knowledge base of reference configurations and reference execution metrics and a particular reference configuration may be identified from the knowledge base that corresponds to the configuration. The particular reference configuration may represent an erroneous configuration of the application that needs to be corrected. A configuration correction may then be identified based on the particular reference configuration.

Abstract: Methods and systems for detecting and correcting inefficient application configurations are presented. In one embodiment, a method is provided that includes receiving, at a first computing environment, a configuration for and execution metrics of an application executed within a second computing environment; identifying, via a machine learning model, a corresponding preferred configuration and expected metrics associated with the corresponding preferred configuration; identifying a difference between at least one of the execution metrics and at least one of the expected metrics; and applying the corresponding preferred configuration to the application within the second computing environment.

Abstract: Methods and systems for detecting and correcting inefficient application configurations are presented. In one embodiment, a method is provided that includes receiving an application and executing the application multiple times according to different configurations. While executing the application, execution metrics may be measured associated with the different configurations. A classifier model may be trained based on the execution metrics and the different configurations. In particular, the classifier model may be trained to identify preferred configurations of the application. The classifier model may be provided to a second computing environment and may be used to identify inefficient configurations for subsequent deployments of the application within the second computing environment. The inefficient configurations may be identified without transmitting configurations or applications from the second computing environment.

Abstract: Methods and systems for selecting, testing, and applying application configurations are presented. In one embodiment, a method is provided that includes executing an application according to a first configuration and measuring a first plurality of metrics. A change to a setting of the first configuration may be identified by a machine learning model to generate a second configuration. The application may be executed according to the second configuration and a second plurality of metrics may be measured. A selected configuration for future executions of the application may be selected from among the first and second configurations based on the first plurality of metrics and the second plurality of metrics.

Abstract: Methods and systems for detecting and correcting inefficient application configurations are presented. In one embodiment, a method is provided that includes receiving an application and executing the application multiple times according to different configurations. While executing the application, execution metrics may be measured associated with the different configurations. A classifier model may be trained based on the execution metrics and the different configurations. In particular, the classifier model may be trained to identify preferred configurations of the application. The classifier model may be provided to a second computing environment and may be used to identify inefficient configurations for subsequent deployments of the application within the second computing environment. The inefficient configurations may be identified without transmitting configurations or applications from the second computing environment.

Abstract: Methods and systems for detecting and responding to erroneous application configurations are presented. In one embodiment, a method is provided that includes receiving a configuration for an application and receiving execution metrics for the application. The configuration and the execution metrics may be compared to a knowledge base of reference configurations and reference execution metrics and a particular reference configuration may be identified from the knowledge base that corresponds to the configuration. The particular reference configuration may represent an erroneous configuration of the application that needs to be corrected. A configuration correction may then be identified based on the particular reference configuration.