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.

Offline task-based feature processing for aerial vehicles is provided. A system can extract features from a world model generated using sensor information captured by sensors mounted on an aerial vehicle. The system generates a label for each of the features and identifies identify processing levels based on the features. The system selects a processing level for each feature of a subset of features based on a task performed by the aerial vehicle and the label associated with the feature. The system generates one or more processed features by applying the processing level to a respective feature of the subset of the plurality of features. The system presents the one or more processed features on a display device of the aerial vehicle.

Existing techniques of managing Electronic Design Automation (EDA) on cloud are based on pre-defined policies which result in costly burst patterns and server farm tilt. Embodiments of present disclosure overcomes these drawbacks by a method and system for managing EDA on cloud which employ machine learning to predict optimal resource configurations for deploying EDA jobs and configuration circuit on cloud that holds resources required by the optimal resource configuration. Further, different Cloud Service Providers (CSP) are evaluated to determine least cost CSP which has the desired configuration circuit. Completion time of jobs and time required to burst the jobs on cloud are calculated based on which a wait time is determined. The jobs are retained in the queue for corresponding wait time before deploying them on the cloud. The jobs are deployed on the on-prem infrastructure if resources are freed up before the wait time.

We disclose a system that executes an inferential model in VRAM that is embedded in a set of graphics-processing units (GPUs). The system obtains execution parameters for the inferential model specifying: a number of signals, a number of training vectors, a number of observations and a desired data precision. It also obtains one or more formulae for computing memory usage for the inferential model based on the execution parameters. Next, the system uses the one or more formulae and the execution parameters to compute an estimated memory footprint for the inferential model. The system uses the estimated memory footprint to determine a required number of GPUs to execute the inferential model, and generates code for executing the inferential model in parallel while efficiently using available memory in the required number of GPUs. Finally, the system uses the generated code to execute the inferential model in the set of GPUs.

In a computing system in which resources are available for performance of computing tasks allocated to them, and tasks are requested by requesting computers, a scheduler is associated with each of the requesting computers, to enable that computer to obtain resource for performance of the tasks. Each scheduler obtains resources, in accordance with a locally formulated preference list of resources, on the basis of scheduling tokens issued by resources indicative of reciprocal prioritisation of tasks by resources.

An exemplary method for using a virtual assistant may include, at an electronic device configured to transmit and receive data, receiving a user request for a service from a virtual assistant; determining at least one task to perform in response to the user request; estimating at least one performance characteristic for completion of the at least one task with the electronic device, based on at least one heuristic; based on the estimating, determining whether to execute the at least one task at the electronic device; in accordance with a determination to execute the at least one task at the electronic device, causing the execution of the at least one task at the electronic device; in accordance with a determination to execute the at least one task outside the electronic device: generating executable code for carrying out the least one task; and transmitting the executable code from the electronic device.

Technologies for dynamic accelerator selection include a compute sled. The compute sled includes a network interface controller to communicate with a remote accelerator of an accelerator sled over a network, where the network interface controller includes a local accelerator and a compute engine. The compute engine is to obtain network telemetry data indicative of a level of bandwidth saturation of the network. The compute engine is also to determine whether to accelerate a function managed by the compute sled. The compute engine is further to determine, in response to a determination to accelerate the function, whether to offload the function to the remote accelerator of the accelerator sled based on the telemetry data. Also the compute engine is to assign, in response a determination not to offload the function to the remote accelerator, the function to the local accelerator of the network interface controller.

A method implemented by a network element (NE) in a mobile edge cloud (MEC) network, comprising receiving, by the NE, an offloading request message from a client, the offloading request message comprising task-related data describing a task associated with an application executable at the client, determining, by the NE, whether to offload the task to an edge cloud server of a plurality of edge cloud servers distributed within the MEC network based on the task-related data and server data associated with each of the plurality of edge cloud servers, transmitting, by the NE, a response message to the client based on whether the task is offloaded to the edge cloud server.

A method of offloading performance of a workload includes receiving, on a first computing system acting as an initiator, a first function call from a caller, the first function call to be executed by an accelerator on a second computing system acting as a target, the first computing system coupled to the second computing system by a network; determining a type of the first function call; and generating a list of parameter values of the first function call.