Embodiments of the invention are directed to a system, method, or computer program product generation and analysis of real-time resource requests via a resource platform. A resource platform is provided for receiving and automating the management and processing of resource requests submitted by entities or users. The system embraces a fully digital approach to resource request processing, analysis, authentication, and reporting. In addition, the invention allows for analysis of reconciliation data of executed resource transfers for identification of useful trends that can be used for proactive accommodation of entity policies to align with perceived user preferences.

Methods, apparatus, systems, and articles of manufacture are disclosed to improve workload domain management of virtualized server systems. An example system includes memory, programmable circuitry, and instructions to program the programmable circuitry to generate a pool of virtualized servers based on a policy, determine whether a utilization of a first virtualized server is less than a first threshold, the first threshold based on at least one type of resource provisionable to the first virtualized server, determine that a firmware status associated with the first virtualized server corresponds to a first firmware version, transfer a workload of the first virtualized server to a second virtualized server after a determination that a second firmware version is available for the first virtualized server, deallocate the first virtualized server from the first workload domain to the pool of the virtualized servers, and update the first virtualized server to the second firmware version.

Embodiments of the present disclosure relate to a method for allocating computing resources, an electronic device, and a corresponding computer program product. The method may include: obtaining an available resource list associated with a computing resource requester according to determination that a resource use request from the computing resource requester is received, wherein the available resource list includes the number and available time periods of computing resources that can be provided by at least one computing resource provider. In addition, the method further includes: allocating computing resources to the computing resource requester based on the available resource list and the resource use request, so that the computing resource requester uses the allocated computing resources to run a workload. The embodiments of the present disclosure can flexibly allocate the computing resources, thereby realizing full utilization of the computing resources.

Examples relate to preventing scheduling or executing a resource on an inconsistent host node in a networked system. Some examples track a taint status of the host node and identify whether the host node is inconsistent based on the taint status of the host node over a predefined period of time. Upon identifying that the host node is inconsistent, a master taint is applied on the inconsistent host node, which prevents scheduling or executing a resource on the identified inconsistent host node.

A telemetry service can receive telemetry collection requirements that are expressed as an “intent” that defines how telemetry is to be collected. A telemetry intent compiler can receive the telemetry intent and translate the high level intent into abstract telemetry configuration parameters that provide a generic description of desired telemetry data. The telemetry service can determine, from the telemetry intent, a set of devices from which to collect telemetry data. For each device, the telemetry service can determine capabilities of the device with respect to telemetry data collection. The capabilities may include a telemetry protocol supported by the device. The telemetry service can create a protocol specific device configuration based on the abstract telemetry configuration parameters and the telemetry protocol supported by the device. Devices in a network system that support a particular telemetry protocol can be allocated to instances of a telemetry collector that supports the telemetry protocol.

Methods, systems, and devices for memory die resource management are described. A resource manager can determine, from a set of global resources for multiple memory dies of a memory sub-system, a set of die-specific resources for a memory die of the multiple memory dies of the memory sub-system. In some case, the set of die-specific resources can be allocated for read commands for the memory die. The resource manager can assign a read command to a die-specific resource of the set of die-specific resources based on the die-specific resource being available and refrain from assigning the read command to the die-specific resource based on the die-specific resource being unavailable.

Techniques for policy and identity-based workload provisioning are presented. Identities for requestors or workloads and identities for workloads are tied to specific policies. The specific policies are evaluated based on a stage of readiness for resources within a resource pool and based on resource identities for the resources within the resource pool. Resources are then dynamically provisioned based on the identity-based policy evaluation to handle workloads from the resource pool.

A managing node includes a processor and a storage unit. A processor rearranges virtual machines arranged in managed nodes with high usage rates to other managed nodes based on a standard deviation of the usage rate of a CPU in a plurality of managed nodes, and selects the managed nodes in which many images of operating systems used by the virtual machine are stored, arranges the virtual machine, and starts up the virtual machine when a client starts up the virtual machine.

System and computer-implemented method for autoscaling clusters of host computers in a cloud-based computing environment uses an aggressive scale-in resource utilization threshold that is greater than a corresponding standard scale-in resource utilization threshold to search for any target clusters of host computers in response to a scale-out recommendation for a cluster of host computers to select a candidate cluster of host computers when the number of available reserved resource instance for the cloud-based computing environment is below a predefined value. A scale-in operation is executed on the candidate cluster of host computers to remove an existing resource instance from the candidate cluster of host computers. A scale-out operation is executed on the cluster of host computers using an available resource instance for the cloud-based computing environment.

Systems and methods are provided for managing task performance for a modular electronic device. In one implementation, a modular electronic device can include one or more electronic modular components. The modular electronic device can identify a computational task associated with the modular electronic device and identify one or more computing devices that are available to perform at least a portion of the computational task. The modular electronic device can obtain one or more sets of data associated with one or more computational resources of the computing devices. The modular electronic device can determine a potential benefit to the modular electronic device associated with the performance of the computational task by the computing devices. The modular electronic device can perform at least a portion of the computational task with the computing devices based, at least in part, on the sets of data associated with the computational resources and the potential benefit.