In an approach, a processor receives a request to deploy a workload in a container environment, where: the container environment comprises a plurality of external providers running container environment clusters; and the request (i) includes one or more requirements of the workload and (ii) does not specify a particular external provider of the plurality of external providers. A processor determines a cluster, from the plurality of external providers running the container environment clusters, that meets the one or more requirements of the workload. A processor deploys the workload on the determined cluster.

An apparatus for a license orchestrator to most efficiently distribute fee-based licenses includes a processor and a memory that stores code executable by the processor to determine that a workload is scheduled to be executed by a computing device. The computing device includes a licensable resource available for execution of the workload. The code is executable to compare a per-use licensing cost associated with using the licensable resource for execution of the workload with a cost of using existing capabilities of the computing device for execution of the workload and license and use the licensable resource for execution of the workload in response to determining that the per-use licensing cost of the licensable resource is less than using the existing capabilities of the computing device.

A system and method include reception of a request to create a virtual machine associated with a requested number of resource units of each of a plurality of resource types, determination, for each of the plurality of resource types, of a pool of available resource units, random selection, for each of the plurality of resource types, of the requested number of resource units from the pool of available resource units of the resource type, and allocation of the selected resource units of each of the plurality of resource types to the virtual machine.

A task scheduling method comprises the steps of: in response to the reception of a request for processing a plurality of task sets, creating a current to-be-scheduled task queue in a task processing system based on priorities of the plurality of task sets and tasks in the plurality of task sets, where a plurality of to-be-scheduled tasks in the current to-be-scheduled task queue are scheduled in the same round of scheduling; allocating computing resources used for scheduling the plurality of to-be-scheduled tasks; and enabling the plurality of to-be-scheduled tasks in the current to-be-scheduled task queue to be scheduled by using the computing resources. In this manner, a plurality of tasks with different priorities and quotas can be scheduled according to SLA levels of users, and the efficiency and flexibility of parallel services of cloud computing deep learning models are improved by using a run-time load-balancing scheduling solution.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: identifying a plurality of asset resources; selecting a workload for allocation of asset resources; determining which asset resources of the plurality of asset resources may be needed for allocation, determination of which asset resources of the plurality of asset resources may be needed for allocation taking into account on-premises asset resources and cloud-based asset resources the inventory of the available asset resources; and, performing a data center hybrid cloud asset allocation operation, the data center asset allocation operation allocating resources the workload based upon the determining.

A workload compliance governor system includes a management system coupled to a computing system. A workload compliance governor subsystem in the computing system receives a workload performance request associated with a workload, exchanges hardware compose communications with the management system to compose hardware components for the workload, and receives back an identification of hardware components. The workload compliance governor subsystem then determines that the identified hardware components satisfy hardware compliance requirements for the workload, and configures the identified hardware components in the computing system based on the software compliance requirements for the workload in order to cause those identified hardware components to provide an operating system and at least one application that operate to perform the workload.

Embodiments of the present invention provide a system for dynamically mapping and routing workload requirements to appropriate computing components. The system is configured for identifying workloads associated with applications of an entity, classifying the workloads based on computational needs associated with the workloads, identifying resources associated with the entity, classifying the resources based on computational capabilities of the resources, performing assessment of the resources and the workloads, assigning each of the workloads to the resources based on performing assessment of the resources and the workloads, receiving real-time workloads associated with the applications, route the real-time workloads to the resources based on the assignment of each of the workloads to the resources.

A workload execution manager receives a request to execute a workload process in a cloud computing environment, where the cloud computing environment comprises a plurality of nodes; identifies a set of eligible nodes of the plurality of nodes for executing the workload process; determines whether a first eligible node of the set of eligible nodes satisfies a version threshold; responsive to determining that the first eligible node satisfies the version threshold, selects the first eligible node as a target node for executing the workload process; and executes the workload process on the target node.

Methods, systems, and apparatuses related to autonomous vehicle object detection are described. A method can include receiving, by an autonomous vehicle, an indication that the autonomous vehicle has entered a network coverage zone generated by a base station and performing an operation to reallocate computing resources between a plurality of different types of memory devices associated with the autonomous vehicle in response to receiving the indication. The method can further include capturing, by the autonomous vehicle, data corresponding to an unknown object disposed within a sight line of the autonomous vehicle and performing, using the reallocated computing resources, an operation involving the data corresponding to the unknown object to classify the unknown object.

Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.