A data management process determines, from user-implemented provisional reservations (400) for data processing resources, a projected total capacity requirement for each said data processing resource, by maintaining a record (9, 90, 91) recording previous such reservations made by each user and comparing each reservations with records (87, 88, 89) of the actual resources used, to provide an estimate of resources required to meet the projected capacity requirement, and to provide data for a demand management processor (2), which control associated configurable data processing equipment (1) to provide the resources required to meet the estimated capacity required. The process takes account of over- and under-ordering of capacity by comparing each reservation (400) with the use actually made (600), and includes a record (10) of ad-hoc (unreserved) usage.

System and techniques for generating a virtual shared resource pool are described herein. The system may include means for reserving, by a controller of a first computing device, a resource on a second computing device. Means for instantiating, by the controller of the first computing device, a local service including a virtual function for the resource. The system may also include means for executing a process on the first computing device using the resource from the second computing device via the virtual function.

Throughput capacity may be changed to sustain throughput for accessing individual items in a database. A table hosted at storage nodes that provide access to the table in a database may be identified as allocated with a client-specified throughput capacity for accessing the table. Performance of access requests to the table at the storage nodes may be tracked. Based on the performance of the access requests, a change may be determined that modifies a throughput capacity for the table to sustain a guaranteed throughput for each access request independent of other access requests received for the table.

A system and method of reserving resources in a compute environment are disclosed. The method embodiment comprises receiving a request for resources within a computer environment, determining at least one completion time associated with at least one resource type required by the request, and reserving resources within the computer environment based on the determine of at least the completion time. A scaled wall clock time on a per resource basis may also be used to determine what resources to reserve. The system may determine whether to perform a start time analysis or a completion time analysis or a hybrid analysis in the process of generating a co-allocation map between a first type of resource and a second type of resource in preparation for reserving resources according to the generated co-allocation map.

Provided are a Central Processing Unit (CPU) resource reservation method, apparatus, and device, and a computer-readable memory medium. The method includes: selecting a target working node according to a received Virtual Machine (VM) startup request; obtaining a total number of virtual cores and a number of allocatable physical cores in the target working node statistically; performing calculation to obtain an available CPU quota according to the total number of virtual cores and the number of allocatable physical cores; and performing CPU resource reservation configuration on the target working node by use of the available CPU quota. According to the CPU resource reservation method, the reservation of CPU resources in a VM system may be implemented more flexibly and efficiently.

The disclosure relates to systems, methods and computer-readable media for dynamically provisioning resources within a compute environment. The method aspect of the disclosure comprises A method of dynamically provisioning resources within a compute environment, the method comprises analyzing a queue of jobs to determine an availability of compute resources for each job, determining an availability of a scheduler of the compute environment to satisfy all service level agreements (SLAs) and target service levels within a current configuration of the compute resources, determining possible resource provisioning changes to improve SLA fulfillment, determining a cost of provisioning; and if provisioning changes improve overall SLA delivery, then re-provisioning at least one compute resource.

The disclosed distributed task coordination ensures task execution while minimizing both the risk of duplicate execution and resources consumed for coordination. Execution is guaranteed, while only best efforts are used to avoid duplication. Example solutions include requesting, by a node, a first lease from a first set of nodes; based at least on obtaining at least one first lease, requesting, by the node, a second lease from a second set of nodes; based at least on the node obtaining at least one second lease, determining a majority holder of second leases; and based at least on obtaining the majority of second leases, executing, by the node, a task associated with the at least one second lease. In some examples, the nodes comprise online processing units (NPUs). In some examples, if a first node begins executing the task and fails, another node automatically takes over to ensure completion.

A system for proactive resource reservation for protecting virtual machines. The system includes a cluster of hosts, wherein the cluster of hosts includes a master host, a first slave host, and one or more other slave hosts, and wherein the first slave host executes one or more virtual machines thereon. The first slave host is configured to identify a failure that impacts an ability of the one or more virtual machines to provide service, and calculate a list of impacted virtual machines. The master host is configured to receive a request to reserve resources on another host in the cluster of hosts to enable the impacted one or more virtual machines to failover, calculate a resource capacity among the cluster of hosts, determine whether the calculated resource capacity is sufficient to reserve the resources, and send an indication as to whether the resources are reserved.

In one embodiment, an apparatus includes: a plurality of execution circuits to execute and instruct micro-operations (μops), where a subset of the plurality of execution circuits are capable of execution of a fused μop; a fusion circuit coupled to at least the subset of the plurality of execution circuits, wherein the fusion circuit is to fuse at least some pairs of producer-consumer μops into fused μops; and a fusion throttle circuit coupled to the fusion circuit, wherein the fusion throttle circuit is to prevent a first μop from being fused with another μop based at least in part on historical information associated with the first μop. Other embodiments are described and claimed.

Self-tuning thread dispatch policies are described herein. According to one example, a self-tuning thread dispatch policy uses the relative execution time for GPU engines from previous frames to modify the thread dispatch policy for a subsequent frame. In one example, a graphics processing device includes command processing circuitry to receive commands for a render engine and a compute engine of the GPU to render and process frames of an application. The graphics processing device also includes circuitry to determine the usage of shared hardware resources by the render engine and the compute engine for one or more frames of the application. The number of threads to dispatch to the shared hardware resources for a next frame can then be adjusted for the render engine or the compute engine based on the usage of the shared hardware resources for the previous one or more frames.