Various techniques are described for classifying synthetic data tasks and orchestrating a resource allocation between groups of eligible resources for processing the synthetic data tasks. Received synthetic data tasks can be classified by identifying a task category and a corresponding group of eligible resources (e.g., processors) for processing synthetic data tasks in the task category. For example, synthetic data tasks can include generation of source assets, ingestion of source assets, identification of variation parameters, variation of variation parameters, and creation of synthetic data. Certain categories of synthetic data tasks can be classified for processing with a particular group of eligible resources. For example, tasks to ingest synthetic data assets can be classified for processing on a CPU only, while a task to create synthetic data assets can be classified for processing on a GPU only. The synthetic data tasks can be queued and routed for processing by an eligible resource.

Techniques are provided for tracking application programming interface (API) requests in a cloud computing environment. For example, a method for tracking API requests is implemented by an API gateway. The API gateway receives an API request which comprises a given API endpoint to access a target service of a computing system. The API gateway determines if the received API request is valid. In response to determining that the received API request is valid, the API gateway accesses at least one API counter associated with the given API endpoint of the received API request, wherein the at least one API counter is configured to count a number of times that the given API endpoint is accessed. The API gateway increments a count of the at least one API counter by one, and the API gateway routes the API request to the target service for execution.

There is provided a model management system that manages, for each computing environment and each service, a model capable of inferring a resource quantity when an application operates, in a resource of the computing environment. The model management system includes an acquiring unit that acquires environment information including at least one of configuration information and setting information of a computing environment from each of a plurality of computing environments, a detecting unit that detects a computing environment in which the environment information acquired by the acquiring unit has been changed, and a selecting unit that selects, as an update target candidate, a model associated with the computing environment detected by the detecting unit.

Embodiments of the present invention provide a system for leveraging distributed register technology to securely monitoring, tracking, and recommending utilization of resources. The system is configured for gathering one or more input parameters from one or more entity systems, collecting activity data from one or more third party systems, analyzing the activity data collected from the one or third party systems, generating one or more recommendations based on the one or more input parameters and analyzing the activity data, wherein the one or more recommendations are associated with one or more activities, estimating resource usage for the one or more recommendations, and allocating resources to the one or more recommendations.

Aspects of the disclosure provide for reducing service disruptions in a computer system. A method of the disclosure may include identifying a plurality of services running on a node of a computer system, determining a plurality of priorities corresponding to the plurality of services, determining a plurality of service capacity factors for the plurality of services in view of the plurality of priorities, and determining a lost impact factor in view of the plurality of service capacity factors.

In non-limiting examples of the present disclosure, systems, methods and devices for providing a unified cross-platform experience are provided. A connection between a first device and a second device may be established, wherein the first device operates on a first platform and the second device operates on a second platform. A plurality of executable actions that are specific to the second device may be identified by the first device. Execution of at least one of the plurality of executable actions by the second device may be requested by the an application executed on the first device. Information obtained via execution of the at least one executable action may be received by the first device and the first device may present and/or display that information.

The present disclosure includes systems, methods, and computer-readable mediums for discovering capabilities of a hardware (HW) accelerator card. A processor may communicate a request for a listing of acceleration services to a HW accelerator card connected to the processor via the communication interface. The HW accelerator card may retrieve the listing from memory and provide a response to the processor that includes a listing of the HW acceleration services provided by the HW accelerator card.

An example system to schedule service requests in a network computing system using hardware queue managers includes: a gateway-level hardware queue manager in an edge gateway to schedule the service requests received from client devices in a queue; a rack-level hardware queue manager in a physical rack in communication with the edge gateway, the rack-level hardware queue manager to send a pull request to the gateway-level hardware queue manager for a first one of the service requests; and a drawer-level hardware queue manager in a drawer of the physical rack, the drawer-level hardware queue manager to send a second pull request to the rack-level hardware queue manager for the first one of the service requests, the drawer including a resource to provide a function as a service specified in the first one of the service requests.

A method for executing a task by a scheduling device, belonging to the technical field of electronics. The method includes: acquiring a target algorithm corresponding to a target task to be executed; acquiring an execution environment condition for a target algorithm, and current execution environment information of various execution devices; in the execution devices, determining a target execution device of which the execution environment information satisfies the execution environment condition; and sending a control message for executing the target task to the target execution device.

Certain embodiments disclosed herein provide application-specific launch optimization. Aspects of the present disclosure include one or more cost functions for each application, where each cost function corresponds to a likelihood that a particular application should be placed into a particular pre-activation state. For each of the inactive applications, a respective one of the pre-activation states is selected based on comparing cost values obtained by evaluating the cost functions. Each of the inactive applications can be moved to or maintained in the respectively-selected pre-activation state to more efficiently provide an expedited application launch experience for a user.