In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a processor and memory storing executable instructions that when executed cause the processor to determine availability of memory resources and processing resources of multiple computing devices. The instructions also cause the processor to form a computing cluster based on the availability of the memory resources and the processing resources. The instructions further cause the processor to assign a computing task to the computing cluster to replace a cloud service.

A processor may automatically select a cloud service provider. A processor may receive one or more parameters associated with an entity and a cloud service dataset associated with a provider. A processor may generate a digital twin of the entity using the one or more parameters. A processor may simulate the digital twin of the entity and the cloud service dataset. A processor may identify, responsive to simulating the digital twin of the entity and the cloud service dataset, one or more predicted conditions of cloud service dataset on the entity. A processor may select the provider based, at least in part, on the one or more predicted conditions.

Methods, systems, and devices for configuring a machine learning network are described. A device, which may be otherwise known as user equipment (UE), may support ultra-low power sensor applications. More specifically, the device may support memory block allocation of a machine learning network based on performance levels associated with the applications. For example, the device may identify a performance level associated with an application on the device. The device may determine that the performance level satisfies a condition, and subsequently determine a memory block allocation of a machine learning network of the device based on the performance level satisfying the condition. The memory block allocation may correspond to one or more connections of the machine learning network. Based on the memory block allocation, the device may adjust a quantity of memory blocks available for the machine learning network and process the application.

Techniques for intelligent compute resource selection and utilization for machine learning training jobs are described. At least a portion of a machine learning (ML) training job is executed a plurality of times using a plurality of different resource configurations, where each of the plurality of resource configurations includes at least a different type or amount of compute instances. A performance metric is measured for each of the plurality of the executions, and can be used along with a desired performance characteristic to generate a recommended resource configuration for the ML training job. The ML training job is executed using the recommended resource configuration.

The present disclosure discloses a workflow scheduling method and system based on a multi-target particle swarm algorithm, and a storage medium. The method comprises the following steps that first, the difference between the frequency reduction characteristic and the execution time of each server in a cluster is considered; a multi-target comprehensive evaluation model covering workflow execution overhead, execution time and cluster load balance is constructed on the basis of a traditional model; second, a multi-target particle swarm algorithm is provided for workflow scheduling, and an efficient solving method is provided. The method alleviates the defects of premature convergence and low species diversity of the particle swarm algorithm, reduces the execution overhead and execution time of the workflow on the cluster server, and better balances the load of the cluster server.

Embodiments are directed to deploying a workload on the best/highest performance node. Nodes configured to accommodate a request for a workload are selected. Information is collected on each of the selected nodes and the workload. Predicted response times expected for the workload running on each of the selected nodes are determined. The workload is deployed on a node of the selected nodes, the node having a corresponding predicted response time for the workload, the workload being deployed on the node based at least in part on the corresponding predicted response time.

A machine learning model is trained. A feature importance metric is determined for each machine learning feature of a plurality of machine learning features of the machine learning model. Based on the feature importance metrics, one or more machine learning features of the plurality of machine learning features of the machine learning model are managed.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform receiving item data for items from a catalog, assigning a task for evaluation of the item data, storing a plurality of task jobs to a task queue, repeatedly setting, in real time, a respective processor to perform a respective evaluation model, processing the plurality of task jobs stored to the task queue by determining, in real time, whether a first evaluation model set to be performed on a first processor is capable of meeting the first evaluation criteria of the first task data, performing, on the first processor, the first evaluation model on the first task data, and transmitting first first-evaluation-model-output instructions, and repeatedly updating, in real time, the task queue. Other embodiments are disclosed herein.

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.

A system is configured for determining a technology stack in a software application to perform a work project. The system receives and evaluates the work based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance of each technology stack is evaluated. The system identifies a first technology stack performing at a level higher than a performance threshold and at a highest performance level among the plurality of technology stacks. The system deploys the first technology stack in the software application to perform the work project.