Abstract: In an example, there is disclosed a computing apparatus, having a computing resource; a bespoke sensor for measuring at least one parameter of usage of the computing resource; and one or more logic elements providing a trusted compute meter (TCM) agent to: receive an external workload; provision a workload enclave; execute the external workload within the TCM enclave; and measure resource usage of the external workload via the bespoke sensor. There is also disclosed a computer-readable medium having stored thereon executable instructions for providing a TCM agent, and a method of providing a TCM agent.

Abstract: In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory stores visual data captured by one or more sensors. The processor detects one or more first objects in the visual data based on a machine learning model and one or more first reference templates. The processor further determines, based on an object ontology, that the visual data is expected to contain a second object, wherein the object ontology indicates that the second object is related to the one or more first objects. The processor further detects the second object in the visual data based on the machine learning model and a second reference template. The processor further determines, based on an inference rule, that the visual data is expected to contain a third object. The processor further detects the third object in the visual data based on the machine learning model and a third reference template.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: In one embodiment, an apparatus comprises circuitry, wherein the circuitry is configured to: transmit, via a communications network, first context information of a first edge device to one or more second edge devices, wherein the first context information identifies an operating environment of the first edge device based on information from one or more sensors; receive, via the communications network, second context information of the one or more second edge devices, wherein the second context information identifies an operating environment of the one or more second edge devices based on information from one or more sensors; and perform a network management function based on the first context information and the second context information.

Abstract: In one embodiment, an apparatus comprises circuitry, wherein the circuitry is configured to: receive, via a communications network, context information for a first set of one or more edge devices, wherein the context information identifies an operating environment of the first set of edge devices based on information from one or more sensors; receive, via the communications network, workload information for a second set of one or more edge devices; determine workload assignments for the first set of edge devices based on the context information for the first set of edge devices and based on the workload information for the second set of edge devices; and transmit, via the communications network, the workload assignments to the first set of edge devices.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory stores visual data captured by one or more sensors. The processor detects one or more first objects in the visual data based on a machine learning model and one or more first reference templates. The processor further determines, based on an object ontology, that the visual data is expected to contain a second object, wherein the object ontology indicates that the second object is related to the one or more first objects. The processor further detects the second object in the visual data based on the machine learning model and a second reference template. The processor further determines, based on an inference rule, that the visual data is expected to contain a third object. The processor further detects the third object in the visual data based on the machine learning model and a third reference template.

Abstract: In an example, there is disclosed a computing apparatus, having a computing resource; a bespoke sensor for measuring at least one parameter of usage of the computing resource; and one or more logic elements providing a trusted compute meter (TCM) agent to: receive an external workload; provision a workload enclave; execute the external workload within the TCM enclave; and measure resource usage of the external workload via the bespoke sensor. There is also disclosed a computer-readable medium having stored thereon executable instructions for providing a TCM agent, and a method of providing a TCM agent.

Abstract: Embodiments of the present invention provide an approach for managing distributed application relationships and dependencies, and migration of applications and components within the same or a disparate networked (e.g. cloud) service provider. An application dependency management program module (“ADM”), and cloud service migration management program module (“CSMM”) are provided. The ADM, manually or by deductive logic, discovers and manages relationships and dependencies of computer software applications underlying cloud services, state information of the respective computer software applications, as well as upstream and downstream network data connections. The CSMM comprises ensuring that applications are not migrated or modified in a manner that could affect overall service, ensuring that a migration is performed correctly, maintaining a list of service level requirements for monitoring whether those requirements are being met, and performing application migration.

Abstract: Embodiments of the present invention provide an approach for managing distributed application relationships and dependencies, and migration of applications and/or of components within the same or a disparate networked (e.g. cloud) service provider which comprise applications, upstream and downstream data, and upstream and downstream network data connections of networked services. An application dependency management program module (“ADM”), and cloud service migration management program module (“CSMM”) are provided. The ADM, manually or by deductive logic, discovers and manages relationships and dependencies of computer software applications underlying cloud services, state information of the respective computer software applications, as well as upstream and downstream network data connections.