A system described herein may provide a technique for the dynamic selection of configurable resources in an environment that includes a hierarchical or otherwise differentiated arrangement of configurable resources. The environment may include, or may be implemented by, a Distributed Resource Network (“DRN”), which may include hardware or virtual resources that may be configured, including the instantiation of containers, virtual machines, Virtualized Network Functions (“VNFs”), or the like. The DRN may be hierarchical in that some resources of the DRN may provide services to, and/or may otherwise be accessible to, a greater quantity of elements of the DRN or some other network.

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.

An allocation control apparatus (1) includes a virtual core allocation unit (112) configured to allocate, out of a virtual core which is created on a virtual machine and occupies a physical core and a virtual core which is created on the virtual machine and shares a physical core, a task whose priority is equal to or greater than a threshold to a virtual core, and an interrupt core allocation unit (113) configured to allocate the virtual core to which the task is allocated as an interrupt destination of a virtual network interface card used by the task.

Approaches presented herein enable dynamic optimization of a degree to which a query is parallelized for execution. More specifically, a priority associated with an obtained user query for execution is identified. A real-time metric indicating availability of one or more runtime resources is checked. An optimal degree of parallelism is calculated based on the priority associated with the obtained user query and the real-time availability metric. A plan is generated for executing the query using the calculated optimal degree of parallelism.

A compute device includes one or more processors, one or more resources capable of being utilized by the one or more processors, and a platform interconnect to facilitate communication of messages between the one or more processors and the one or more resources. The compute device is to obtain class of service data for one or more workloads to be executed by the compute device. The class of service data is indicative of a capacity of one or more of the resources to be utilized in the execution of each corresponding workload. The compute device is also to execute the one or more workloads and manage the amount of traffic transmitted through the platform interconnect for each corresponding workload as a function of the class of service data as the one or more workloads are executed.

Systems and methods are provided for improving compute job distribution using federated computing nodes. This includes identifying a plurality of independently controlled computing nodes which then receive a token such that they can each be identified as being authorized to participate in a federated computing node cluster. Metrics associated with the particular nodes are then received and based on the received metrics compute jobs are assigned to the particular node by assembling a compute job data packet comprising the one or more compute jobs and transmitting the assembled compute job data packet to the particular node. Other features are also described in which assigned compute jobs and/or unrelated compute tasks can be dynamically modified in order to optimize compute job completion based on the received metrics.

Improved techniques are disclosed for processing transactions or requests in a data processing system. For example, a method for processing requests in a system including a plurality of nodes includes the following steps. At least two nodes of the plurality of nodes receive a plurality of requests. The two nodes exchange information to determine an order for processing requests. The two nodes process the requests in accordance with the order. The order may include a total order or a partial order.

A prioritization system includes a memory that stores an access record with, for each of the users, an indication of a previous usage of computing applications. The memory stores a permission record with, for each of the users, an indication of the computing applications that the user is permitted to access. The memory stores user affinities that include, for each of the users, an affinity score corresponding to a predetermined ability level of the user to engage in an activity associated with one or more of the computing applications. The prioritization system determines a priority score for each of the users. In response to receiving a request for a priority of a first user of the users, the prioritization system provides a response with the priority score determined for the first user of the users.

The present invention relates to methods and systems for allocating platform resources in a vehicle for development, evaluation, and/or testing of ADS features. The method comprises storing, during a time period, sensor data indicative of a surrounding environment of the vehicle in a data storage device of the vehicle, and obtaining data indicative of a set of platform constraints of the vehicle, a set of requirements for each of a plurality of ADS features, a priority scheme for the plurality of ADS features, and a current scene or scenario in the surrounding environment of the vehicle. Furthermore, the method comprises generating, based on the platform constraints, the set of requirements, the priority scheme and the current scene or scenario, an arbitration signal indicative of a resource allocation of the platform of the vehicle to at least one of the plurality of ADS features.

A method, computer program product, and computing system for: receiving an indication that a specific request has been completely processed on an IT computing device; analyzing a plurality of queued requests within a pending queue of the IT computing device to identify a target queued request for processing based, at least in part, upon: a root request limit of the IT computing device, a global request limit of the IT computing device, and a sibling status of the specific request.