Abstract: According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.

Abstract: According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.

Abstract: According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.

Abstract: According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.

Abstract: In one implementation, a cloud resource system includes a resource engine and configuration engine. The resource engine can identify a cloud resource based on a template and assign the cloud resource based on a subscription and the template. The configuration engine can maintain a pool of cloud resource object instances.

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: In one example in accordance with the present disclosure, a method may include classifying each word in a natural language statement and determining an implementation, from a set of possible implementations, for a workflow platform based on the classified words. The method may also include mapping a first of the classified words to a task selected from a set of possible tasks associated with the implementation and mapping a second of the classified words to an input parameter associated with the task. The method may also include generating a workflow for the workflow platform using the task and the input.

Abstract: Examples described relate to managing a software delivery pipeline. In an example, a modified software may be identified. Further, a modification in the modified software may be identified. Further, a type of the modification may be identified. Further, a stage of a software delivery pipeline management system may be identified based on the type of the modification. Further, the modified software may be placed in the identified stage of the software delivery pipeline management system.

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: Provided is a method of identifying a computing resource requiring monitoring for a security purpose in a cloud-based data center during creation of a service template involving the computing resource. The identified computing resource is depicted in the service template. Upon receipt of a request for creating a service instance based on the service template, the service instance is created based on the service template and the identified computer resource is simultaneously monitored for the security purpose.

Abstract: Examples described relate to managing a software delivery pipeline. In an example, a modified software may be identified. Further, a modification in the modified software may be identified. Further, a type of the modification may be identified. Further, a stage of a software delivery pipeline management system may be identified based on the type of the modification. Further, the modified software may be placed in the identified stage of the software delivery pipeline management system.

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: In one example in accordance with the present disclosure, a method may include classifying each word in a natural language statement and determining an implementation, from a set of possible implementations, for a workflow platform based on the classified words. The method may also include mapping a first of the classified words to a task selected from a set of possible tasks associated with the implementation and mapping a second of the classified words to an input parameter associated with the task. The method may also include generating a workflow for the workflow platform using the task and the input.

Abstract: In one implementation, a cloud resource system includes a resource engine and configuration engine. The resource engine can identify a cloud resource based on a template and assign the cloud resource based on a subscription and the template. The configuration engine can maintain a pool of cloud resource object instances.

Abstract: Examples disclosed herein relate to application deployment instructions to receive a new version of an application, select, from a pool of available virtual machines, a target virtual machine comprising a prior version of the application, and deploy the new version of the application to the target virtual machine.

Abstract: Provided is a method of identifying a computing resource requiring monitoring for a security purpose in a cloud-based data center during creation of a service template involving the computing resource. The identified computing resource is depicted in the service template. Upon receipt of a request for creating a service instance based on the service template, the service instance is created based on the service template and the identified computer resource is simultaneously monitored for the security purpose.

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.

Abstract: Allocation of physical resources is achieved by accessing consumption data for each of a plurality of application components executing in one or more virtual machines and consuming a plurality of allocated physical resources. The consumption data is indicative of consumption levels by each of the plurality of application components of each of the plurality of physical resources. Following a determination that a value for a performance metric associated with the application has crossed an associated threshold value, the consumption data is analyzed to identify a consumption level of a first of the plurality of physical resources being consumed by a first of the plurality of application components has deviated from a historical trend for that physical resource. An instruction is then communicated that when executed will cause a change in an allocation level of the first of the plurality of physical resources.

Abstract: Provided is a method of dynamically managing a cloud computing infrastructure. A cloud computing infrastructure is configured on a cloud service provider based on a parameter. The parameter is monitored for a variation.