Abstract: According to examples, an apparatus may include a processor that may generate automated and dynamic fail-fast testing pipelines that are efficiently executed to quickly identify tests for which changes to components such as an application will likely fail. The processor may train a classifier to predict whether changes to the application will fail a test procedure and use the classifier to generate machine-learned predictions of test outcomes to generate failure probabilities. The testing pipeline may be dynamically re-ordered based on the failure probabilities. The processor may also group the test procedures into lifecycle stages. Historical performance data may be used to identify time-limits by which to complete the test procedures of each lifecycle stage. Thus, the generated dynamic testing pipelines may be generated based on the likelihood of failures and test procedure duration.

Abstract: According to examples, an apparatus may include a processor that may generate automated and dynamic fail-fast testing pipelines that are efficiently executed to quickly identify tests for which changes to components such as an application will likely fail. The processor may train a classifier to predict whether changes to the application will fail a test procedure and use the classifier to generate machine-learned predictions of test outcomes to generate failure probabilities. The testing pipeline may be dynamically re-ordered based on the failure probabilities. The processor may also group the test procedures into lifecycle stages. Historical performance data may be used to identify time-limits by which to complete the test procedures of each lifecycle stage. Thus, the generated dynamic testing pipelines may be generated based on the likelihood of failures and test procedure duration.

Abstract: A computing device includes a storage device and a controller. The storage device is to store an application model and infrastructure models. The application model defines a first application and specifies a first communication protocol. The controller is to: determine a current lifecycle stage of the first application defined by the application model; select, from the plurality of infrastructure models, a first infrastructure model based on the current lifecycle stage of the first application; initiate a first application environment based on the selected first infrastructure model and the first communication protocol specified in the application model; and deploy the first application on the first application environment using the first communication protocol.

Abstract: According to an example, to tune a cloud service, an event and an event type on a cloud server application are detected by monitoring at least one of a hardware value and a software value. An application parameter to tune is determined based on the event type, and a tuning priority of the application parameter based on historical tuning data is fetched. The application parameter is tuned by modifying a software variable. In the event that tuning the application parameter resolves the event type, the tuning priority of the parameter is increased. In the event that tuning the parameter fails to resolve the event type, the parameter priority is decreased, the tuning is rolled back, and a default tune setting is invoked.

Abstract: A computing device includes a storage device and a controller. The storage device is to store an application model and infrastructure models. The application model defines a first application and specifies a first communication protocol. The controller is to: determine a current lifecycle stage of the first application defined by the application model; select, from the plurality of infrastructure models, a first infrastructure model based on the current lifecycle stage of the first application; initiate a first application environment based on the selected first infrastructure model and the first communication protocol specified in the application model; and deploy the first application on the first application environment using the first communication protocol.

Abstract: A method includes deploying an application on a target virtual resource environment that includes at least one virtual machine for an associated lifecycle stage of the application. Deploying the application includes selecting a given physical resource environment to support the target virtual resource environment from a plurality of physical resource environments based at least in part on the lifecycle stage and a predefined physical resource environment-to-lifecycle stage mapping.

Abstract: In one implementation, a system for a common deployment model includes a content engine to embrace content from a number of deployment tools, a properties engine to associate a number of properties from the content to generate a component model for the number of deployment tools, a cost engine to associate the component model with a cost model, and a fulfillment engine to instantiate the component model with the associated cost model.

Abstract: Examples disclosed herein relate to automated development operations (DevOps) application deployment. Some examples disclosed herein may include generating a DevOps application deployment packages for DevOps applications based on DevOps application models for deploying the DevOps applications. Application deployment tools for deploying the DevOps applications may be determined based on the DevOps application deployment packages. The DevOps application deployment packages may be provided deployment tool plugins associated with the determined application deployment tools and the deployment tool plugins may execute deployment operations based on deployment properties included in the DevOps application deployment packages to deploy DevOps applications using the determined application deployment tools.

Abstract: Examples relate to supporting interoperability in cloud environments. In some examples, an application topology is converted to a cloud product topology that supports a cloud product standard, where the application topology includes a general application and supports a cloud industry standard. Artifacts associated with the general application are imported into a product database, where the artifacts are exposed in the product database via a standard Internet protocol. At this stage, the cloud product topology is imported into the product database to obtain an imported topology, and the general application is deployed from the imported topology to a server computing device that supports the cloud product standard, where the general application accesses the artifacts via the standard Internet protocol after deployment.

Abstract: According to an example, to tune a cloud service, an event and an event type on a cloud server application are detected by monitoring at least one of a hardware value and a software value. An application parameter to tune is determined based on the event type, and a tuning priority of the application parameter based on historical tuning data is fetched. The application parameter is tuned by modifying a software variable. In the event that tuning the application parameter resolves the event type, the tuning priority of the parameter is increased. In the event that tuning the parameter fails to resolve the event type, the parameter priority is decreased, the tuning is rolled back, and a default tune setting is invoked.

Abstract: According to an example, to provide cloud service ratings, a service type is monitored on a first and second cloud service by capturing on a network interface at least one resource utilization feed and at least one connection feed. Infrastructure-level metrics and application-level metrics for the service type on the first and second cloud services are determined based on the monitoring of the first and second cloud services. Cost information for the service type on the first and second cloud services is fetched. A rating for the service type on the first cloud service and a rating for the service type on the second cloud service are calculated based a weighting of the infrastructure-level metrics, the application-level metrics, and the cost information.

Abstract: Implementations of the present disclosure provide a virtual machine resource management system and method thereof. According to one implementation, a request for service provisioning is received and at least one virtual machine associated with the request is created. When a determination has been made that the allocated virtual resources have exceeded a threshold value, a virtual machine is modified based on an associated life cycle stage priority or service information.

Abstract: In one implementation, a system for a common deployment model includes a content engine to embrace content from a number of deployment tools, a properties engine to associate a number of properties from the content to generate a component model for the number of deployment tools, a cost engine to associate the component model with a cost model, and a fulfillment engine to instantiate the component model with the associated cost model.