Disclosed embodiments relate to software control of graphics hardware that supports logical slots. In some embodiments, a GPU includes circuitry that implements a plurality of logical slots and a set of graphics processor sub-units that each implement multiple distributed hardware slots. Control circuitry may determine mappings between logical slots and distributed hardware slots for different sets of graphics work. Various mapping aspects may be software-controlled. For example, software may specify one or more of the following: priority information for a set of graphics work, to retain the mapping after completion of the work, a distribution rule, a target group of sub-units, a sub-unit mask, a scheduling policy, to reclaim hardware slots from another logical slot, etc. Software may also query status of the work.

The present disclosure describes a technique for honoring virtual machine placement constraints established on a first host implemented on a virtualized computing environment by receiving a request to migrate one or more virtual machines from the first host to a second host and without violating the virtual machine placement constraints, identifying an architecture of the first host, provisioning a second host with an architecture compatible with that of the first host, adding the second host to the cluster of hosts, and migrating the one or more virtual machines from the first host to the second host.

Systems and systems for hybrid centralized distributive scheduling and conflict resolution of multiple scheduler instances that share physical resources in a cloud computing system. The cloud computing system includes a plurality of scheduler instances, a global resource manager (GRM) for high-level resource management and conflict resolution for the scheduler instances, and a plurality of physical hosts. Each physical host has a respective local resource manager (LRM). The scheduler instances are responsible for initially processing of scheduling and resource allocation for resource requests, and proposing candidate physical hosts (and respective resource allocation) for the resource requests to the GRM. The GRM is responsible for conflict resolution through its general conflict resolvers of filtering, sorting and counting. The GRM decides which physical hosts among the candidate physical hosts will run the runtime instances of the resource requests after resolving conflicts among the scheduler instances.

The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

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.

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.

A resource processing method includes: determining a current application scenario and usage data of the mobile terminal; inputting the usage data into a machine learning algorithm model corresponding to the current application scenario to obtain predicted recommendation parameters; and configuring resources of the mobile terminal based on the recommendation parameters.

Systems and methods for enabling various devices to remotely interact with cloud-based client applications are provided. A method comprises receiving a first request from a first client device of a user to initiate an interactive session with a cloud-based client application, reserving an application engine for executing the cloud-based client application remotely from the first client device, receiving interaction data from the first client device as the user engages with a first media data associated with the cloud-based client application, modifying the cloud-based client application executing within the application engine that is reserved based on the interaction data received from the first client device, receiving a second request from the first client device to end the interactive session with the cloud-based client application that is modified, and deallocating the application engine that is reserved, wherein the application engine that is reserved is delinked from the first client device.

Methods, computer program products, and/or systems are provided that perform the following operations: identifying, in an environment that includes a plurality of edge clusters of edge nodes, a first edge cluster having a resource gap; broadcasting a resource requirement of the first edge cluster to other edge clusters in the plurality; obtaining resource commitments from one or more of the other edge clusters; selecting edge cluster resources from the one or more of the other edge clusters based, at least in part, on the resource commitments; and creating a new cluster including the first edge cluster and the selected edge cluster resources.

A method, a device, and a computer program product for resource scheduling is disclosed. The method includes determining a job initiated by a virtual machine. The job requests to invoke at least one virtual function in a set of virtual functions associated with the virtual machine and each virtual function in the set of virtual functions is configured to utilize an accelerator resource to provide a single type of acceleration service. The method further includes determining, based on a job type of the job, a first accelerator resource allocated to the at least one virtual function. The accelerator resources required by the virtual functions invoked by the job may then be guaranteed, improving the execution efficiency of the job.