A device may receive a job request associated with a data processing job, including job timing data specifying a time at which the data processing job is to be executed by a virtual computing environment. The device may receive user data associated with the job request and validate the data processing job based on the user data. In addition, the device may identify a priority associated with the data processing job, based on the user data and the job timing data. The device may provide, to a job queue, job data that corresponds to the data processing job, and monitor the virtual computing environment to determine when virtual resources are available. The device may also determine, based on the monitoring, that a virtual resource is available and, based on the determination and the priority, provide the virtual resource with data that causes execution of the data processing job.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for optimized resource transformation given a set of resource optimization parameters. In accordance with one embodiment, a method is provided that includes: identifying a demand surge scenario associated with resource demand conditions; in response to identifying the scenario: determining a downgrade set of resources; determining whether a downgrade-only resource transformation scenario characterized by downgrade transformation of the downgrade set satisfies the conditions; and responsive to determining the downgrade-only resource transformation scenario fails to satisfy the conditions: identifying residual resources that are transformable to meet the conditions, processing the residual resources using a machine learning model characterized by the set of resource optimization parameters to generate resource priority scores, and generating a optimized resource transformation scenario from a scenario based at least in part on the resource priority scores and the downgrade-only resource transformation scenario.

An architecture for a load-balanced groups of multi-stage manycore processors shared dynamically among a set of software applications, with capabilities for destination task defined intra-application prioritization of inter-task communications (ITC), for architecture-based ITC performance isolation between the applications, as well as for prioritizing application task instances for execution on cores of manycore processors based at least in part on which of the task instances have available for them the input data, such as ITC data, that they need for executing.

A mainframe computing system hosts a plurality of logical partitions, each having a static entitlement of processing capacity. The mainframe computer system has a workload manager that schedules work requested by the logical partitions and tracks consumption of the processing capacity by the logical partitions, and a capping policy that is stored in non-transitory memory and which identifies a subset of the logical partitions. The mainframe computer system further includes a capping master that is configured to allocate dynamically varying entitlements of processing capacity to the subset of the logical partitions based on the high-importance work percentages of computing workloads running on the logical partitions to encourage completion of high-importance work over completion of low-importance work. The capping master limits the allocated dynamic entitlement amount in millions of service units per hour (MSU) for each system usage entity to be no greater than the static entitlement of the system usage entity.

A priority-based resource allocation method, includes accepting a resource application submitted by a job, the resource application including resource demand information and job priority information; determining, according to the resource demand information of the resource application, whether remaining resources of a system meet the resource application, and traversing, in an allocated resource application queue when the remaining resources do not meet the resource application, allocated resource applications having job priorities lower than that of the resource application; using the sum of system resources occupied by all traversed resource applications plus the remaining resources as available resources; and stopping traversing when the available resources meet the resource application, and allocating the available resources to the resource application. The technical solution of the present disclosure enables a resource application having a high job priority to preempt resources of a resource application having a low job priority.

The present application reveals a system for computing and a method for arranging nodes thereof, which is applied for a remote host connected with a plurality of computing nodes divided to a plurality of first nodes and second nodes due to a first computing mode and a second computing mode. After the remote host receives a job, the remote host evaluates the computing nodes in accordance with the job and a corresponding priority weight parameter to generate a job beginning data to set the first nodes or the second nodes and to proceed the job. While setting the first or the second nodes, the remote host provides the corresponding system image to the corresponding nodes; while the first or the second nodes are full in resource arrangement, the empty nodes will be converted to the supplement nodes with the corresponding system image from the remote host.

Disclosed a device scheduling method. A Task Description (TD) in a task queue is read and parsed, to acquire task information of a task corresponding to the TD; and when it is determined that the task has met a starting condition and the task is a task with a highest priority among tasks which currently meet the starting condition, a preset parameter is acquired according to the task information, and the parameter is configured to a device intended to complete the task. A task manager and a storage medium is also disclosed.

An architecture for a load-balanced groups of multi-stage manycore processors shared dynamically among a set of software applications, with capabilities for destination task defined intra-application prioritization of inter-task communications (ITC), for architecture-based ITC performance isolation between the applications, as well as for prioritizing application task instances for execution on cores of manycore processors based at least in part on which of the task instances have available for them the input data, such as ITC data, that they need for executing.

Embodiments are provided for upgrading operating application in a multi-device ecosystem in a computing environment. Various types of computing devices are determined to be connected to a multi-device computing network. A collaboration plan is generated between the computing devices to execute an operating application operation event on each of the computing devices without interrupting user activities executing on each of the computing devices. Operating applications on each of the computing devices are upgraded according to the collaboration plan without interrupting each of the f user activities on each of the computing devices.

A networking device continuously and simultaneously receives multiple sign and verify requests, without a priori knowledge of their quantity, type, sequence, length, input data or frequency. The networking device performs the corresponding signature operations and verification operations according to a rule based configuration. Each received sign and verify request is broken into multiple tasks, which are placed into a task pool with priorities, and disassociated from their originating requests and from other tasks. Multiple execution agents (each one a separate general purpose compute unit with its own memory space) repeatedly and simultaneously processes next available tasks from the pool based on priority. Asynchronous worker routines in the agents can pre-calculate certain values for higher level task processing. The output from completed tasks is placed into a repository, and the results in the repository are processed to fulfill the multiple received requests at network line speed.