Abstract: Examples disclosed herein relate to control of a drone. In one example, aerial movement of the drone is controlled. In the example, it is determined, based on a plurality of devices, whether the drone is within a line-of-sight with at least a respective one of a plurality of humans within a physical proximity to a respective one of the devices. In the example, the devices are used by the drone to track the humans. In the example, when the drone is determined to lack the line-of-sight, aerial movement of the drone is controlled to move the drone to become within the line-of-sight.

Abstract: First resources of a first cloud, a first dependency between the first resources, and second resources of a second cloud may be automatically discovered. Second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources. The migration map may be recursively modified to increase accuracy of the migration map. The first resources may be migrated to the second cloud based on the modified migration map.

Abstract: Examples disclosed herein relate to control of a drone. In one example, aerial movement of the drone is controlled. In the example, it is determined, based on a plurality of devices, whether the drone is within a line-of-sight with at least a respective one of a plurality of humans within a physical proximity to a respective one of the devices. In the example, the devices are used by the drone to track the humans. In the example, when the drone is determined to lack the line-of-sight, aerial movement of the drone is controlled to move the drone to become within the line-of-sight.

Abstract: Examples disclosed herein relate to control of a drone. In one example, aerial movement of the drone is controlled. In the example, it is determined, based on a plurality of devices, whether the drone is within a line-of-sight with at least a respective one of a plurality of humans within a physical proximity to a respective one of a the devices. In the example, the devices are used by the drone to track the humans. In the example, when the drone is determined to lack the line-of-sight, aerial movement of the drone is controlled to move the drone to become within the line-of-sight.

Abstract: A method of policy-based scaling of network resources comprises, with a processor, creating a dependency data set for a number of application components on a network, setting a number of scaling policies by tier based on the dependency data set, and applying the scaling policies within the network. A cloud management device for policy-based scaling of network resources comprises a processor, and a data storage device communicatively coupled to the processor, in which the processor creates a dependency data set for a number of application components on a network, and sets a number of scaling policies by tier based on the dependency data set.

Abstract: A method includes generating firewall port access rules between a first cloud system a second cloud system for each tenant of a plurality of tenants. A unique IP address range is generated for each tenant. The firewall port access rules are applied to each IP address.

Abstract: A method for managing data in a distributed computing environment comprises receiving, on a message bus, a request to communicate data between a first system hosting data and a second system accessing data, referencing a policy defining rules for sharing data between the first system and the second system, compiling, based on the policy, a message indicating subscribers to receive the data to publish, and publishing, on the message bus, the compiled message.

Abstract: A method of policy-based scaling of network resources comprises, with a processor, creating a dependency data set for a number of application components on a network, setting a number of scaling policies by tier based on the dependency data set, and applying the scaling policies within the network. A cloud management device for policy-based scaling of network resources comprises a processor, and a data storage device communicatively coupled to the processor, in which the processor creates a dependency data set for a number of application components on a network, and sets a number of scaling policies by tier based on the dependency data set.

Abstract: Repository data fragments distributed across one or both of a first cloud and a network may be accessed. The repository data fragments may be combined into repository data. First resources of the first cloud, a dependency between the first resources, and second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources and based on the repository data. The first resources may be migrated to the second cloud based on the migration map.

Abstract: Examples disclosed herein relate to control of a drone. In one example, aerial movement of the drone is controlled. In the example, it is determined, based on a plurality of devices, whether the drone is within a line-of-sight with at least a respective one of a plurality of humans within a physical proximity to a respective one of a the devices. In the example, the devices are used by the drone to track the humans. In the example, when the drone is determined to lack the line-of-sight, aerial movement of the drone is controlled to move the drone to become within the line-of-sight.

Abstract: A method of policy-based scaling of network resources comprises, with a processor, creating a dependency data set for a number of application components m a network, setting a number of scaling policies by tier based on the dependency date set, and applying the scaling policies within the network. A cloud management device for policy-based scaling of network resources comprises a processor, and a data storage device communicatively coupled to the processor, in which the processor creates a dependency data set for a number of application components on a network, and sets a number of scaling policies by tier based on the dependency data set.

Abstract: In one implementation, a method comprises receiving an objective of a business, searching an application repository to automatically identify an application in the application repository that is associated with the received objectives, receiving metrics for the application and outputting an objective evaluation of the application based on the received metrics and the received objectives. In one implementation, usage of a plurality of applications is monitored to identify usage patterns for each of the plurality of applications. The identified usage patterns are compared with received business objectives and an objective evaluation is output for each of the applications based upon the comparison, the objective evaluation serving as a basis for maintaining, discontinuing or re-architecting the applications.

Abstract: A method and apparatus receive an input indicating system service requirements. A repository is searched based upon the system service requirements to identify a solution model, the solution model comprising a framework of functional elements for the system service requirements. Criteria regarding the identified functional elements are received, and archetype and archetype products are identified based on the functional elements and the criteria. A system blueprint is generated comprising the archetype. products. The primary system blueprint comprises an application model and an infrastructure model, wherein the application model comprises a listing of archetype products and configuration settings for each archetype product and wherein the infrastructure model comprises specifications for an infrastructure connecting the archetype products.

Abstract: A method includes generating firewall port access rules between a first cloud system a second cloud system for each tenant of a plurality of tenants. A unique IP address range is generated for each tenant. The firewall port access rules are applied to each IP address.

Abstract: A method of migrating a number of applications from a first network to a second network comprises, with a processor, creating a dependency data set for a first network, creating a migration plan to migrate a number of applications from the first network to a second network based on the dependency data set, and migrating a number of applications from the first network to the second network based on the migration plan. A migration device for migrating a number of applications from a first network to a second network, comprises a processor, and a data storage device communicatively coupled to the processor, in which the processor creates a dependency data set for the first network, and migrates a number of applications from the first network to the second network based on the dependency data set.

Abstract: First resources of a first cloud, a first dependency between the first resources, and second resources of a second cloud may be automatically discovered. Second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources. The migration map may be recursively modified to increase accuracy of the migration map. The first resources may be migrated to the second cloud based on the modified migration map.

Abstract: A method for managing data in a distributed computing environment comprises receiving, on a message bus, a request to communicate data between a first system hosting data and a second system accessing data, referencing a policy defining rules for sharing data between the first system and the second system, compiling, based on the policy, a message indicating subscribers to receive the data to publish, and publishing, on the message bus, the compiled message.

Abstract: Example implementations relate to using a generic model to provision a computing resource to implement a cloud computing service. For example, a computing device may include a processor. The processor may generate a generic model of a cloud computing service. The generic model may be independent of a technology implementation of the cloud computing service. The processor may also generate a logical model of the cloud computing service based on the generic model. The processor may further provision a computing resource to implement the cloud computing service based on the logical model.

Abstract: Example implementations relate to managing tasks for a user. In example implementations, semantic tags may be detected in electronic documents that are accessed by a user. Models of the accessed electronic documents may be generated based on semantic analysis. A priority of a task for the user may be determined based on the detected semantic tags and the generated models.

Abstract: Repository data fragments distributed across one or both of a first cloud and a network may be accessed. The repository data fragments may be combined into repository data. First resources of the first cloud, a dependency between the first resources, and second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources and based on the repository data. The first resources may be migrated to the second cloud based on the migration map.