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: A system includes a marketplace portal to allow a user to order a business service, and a cloud controller to deploy an instance of the ordered business service to a cloud environment. The system includes an application programming interface (API) scanner to discover API information including at least one API exposed by the deployed instance of the ordered business service, and publish the discovered API information to another service.

Abstract: A system includes a marketplace portal to allow a user to order a business service, and a cloud controller to deploy an instance of the ordered business service to a cloud environment. The system includes an application programming interface (API) scanner to discover API information including at least one API exposed by the deployed instance of the ordered business service, and publish the discovered API information to another service.

Abstract: A system includes a federated marketplace portal to allow a user to browse, order, and manage business services offered by a plurality of cloud marketplaces based at least in part on application programming interface (API) abstractions. The system includes an integration system to transform the API abstractions into product-specific APIs used by the cloud marketplaces and cause the invocation of the product-specific APIs.

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: In example implementations, an apparatus is provided. The apparatus includes an interface, an inheritance engine, an anomaly detection module and a processor. The interface communicates with a sensor array. The inheritance engine is used to create a model of the sensor array based on information collected from the each one of a plurality of nodes in the sensor array over the interface. The anomaly detection engine is used to monitor the sensor array in accordance with the model to detect an anomaly and initiate a corrective action to correct the anomaly in two or more of the plurality of nodes within the sensor array simultaneously. The processor is in communication with the inheritance engine and the anomaly detection engine is used to execute instructions associated with the inheritance engine and the anomaly detection engine.

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: Example implementations relate to a drug dispenser. For example, a dispenser may include a processor in communication with a server accessible to a doctor capable of prescribing a drug to a patient and a pharmacist capable of providing the drug to the patient. The processor may receive instructions relating to administration of the drug to the patient. The instructions may specify a timing and an amount of the drug to be administered and an identity of the patient. The dispenser may include a timer to provide a notification based on the timing specified in the instructions and an identifier mechanism to determine an identity of a person attempting to access the drug. The processor may provide the amount of the drug based on the instructions if the identity of the person is the identity of the patient.

Abstract: Examples associated with data validation are disclosed. One example includes a sensor attribute data store having information describing attributes of a set of distributed sensors. A pattern data store stores information describing patterns indicating anomalous sensor activity. A data aggregation module flags data received from a tested sensor as anomalous data when the anomalous data exceeds a variance level described by an attribute of the tested sensor. A data validation module validates the anomalous data by comparing the anomalous data to the patterns indicating anomalous sensor activity. A learning module updates the pattern indicating anomalous sensor activity based on a result received from the validation logic after the validation logic validates data received from the tested sensor.

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 system includes a policy manger that includes a policy to describe policy attributes of an application that define whether the application can be deployed as a container server or as a non-container server. An application analyzer analyzes a given application with respect to the policy attributes to classify the given application as a container model or a non-container model. A deployment controller generates a corresponding container server for the given application if the given application is classified as a container model or generates a corresponding non-container server for the given application if the given application is classified as a non-container model.

Abstract: Examples include cloud access rule translation for a hybrid cloud computing environment. Some examples include translation of a cloud access rule in a cloud-specific format to a canonical format and a determination of whether to allow an application programming interface (API) request for a cloud computing service based on the translated cloud access rule.

Abstract: A target model is determined for a source application that is to be migrated to a target system. The target model can be determined in part from policies and application characteristics of the source application. A deployment plan is determined, based at least in part on the target model, for application functionality that represents a migration of the source application. The deployment plan can include executable instructions to implement multiple deployment objectives of the application functionality with the target system. The instructions of the deployment plan can also include recursive logic to select or configure at least one deployment objective based on an outcome of implementing another deployment objective with the target system.

Abstract: Lifecycle management in a cloud environment is disclosed. The lifecycle management includes accessing lifecycle actions of an orchestration from a service registry. The orchestration is executed in the cloud environment. Lifecycle actions not available in the service registry are non-deterministically injected into the orchestration.

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 system includes a policy manger that includes a policy to describe policy attributes of an application that define whether the application can be deployed as a container server or as a non-container server. An application analyzer analyzes a given application with respect to the policy attributes to classify the given application as a container model or a non-container model. A deployment controller generates a corresponding container server for the given application if the given application is classified as a container model or generates a corresponding non-container server for the given application if the given application is classified as a non-container model.

Abstract: A system includes a federated marketplace portal to allow a user to browse, order, and manage business services offered by a plurality of cloud marketplaces based at least in part on application programming interface (API) abstractions. The system includes an integration system to transform the API abstractions into product-specific APIs used by the cloud marketplaces and cause the invocation of the product-specific APIs.

Abstract: In example implementations, an apparatus is provided. The apparatus includes an interface, an inheritance engine, an anomaly detection module and a processor. The interface communicates with a sensor array. The inheritance engine is used to create a model of the sensor array based on information collected from the each one of a plurality of nodes in the sensor array over the interface. The anomaly detection engine is used to monitor the sensor array in accordance with the model to detect an anomaly and initiate a corrective action to correct the anomaly in two or more of the plurality of nodes within the sensor array simultaneously. The processor is in communication with the inheritance engine and the anomaly detection engine is used to execute instructions associated with the inheritance engine and the anomaly detection engine.

Abstract: A system includes a marketplace portal to allow a user to order a business service, and a cloud controller to deploy an instance of the ordered business service to a cloud environment. The system includes an application programming interface (API) scanner to discover API information including at least one API exposed by the deployed instance of the ordered business service, and publish the discovered API information to another service.

Abstract: Examples associated with data validation are disclosed. One example includes a sensor attribute data store having information describing attributes of a set of distributed sensors. A pattern data store stores information describing patterns indicating anomalous sensor activity. A data aggregation module flags data received from a tested sensor as anomalous data when the anomalous data exceeds a variance level described by an attribute of the tested sensor. A data validation module validates the anomalous data by comparing the anomalous data to the patterns indicating anomalous sensor activity. A learning module updates the pattern indicating anomalous sensor activity based on a result received from the validation logic after the validation logic validates data received from the tested sensor.