In some examples, a system associates, with a plurality of virtual resources deployed in a cloud environment, properties representative of characteristics of the virtual resources, the properties comprising a performance level of a virtual resource. The system receives a request to create a virtual resource in the cloud environment, and, in response to determining that properties of the virtual resource to be created for the request satisfy a criterion with respect to properties of a given virtual resource of the plurality of virtual resources, selects the given virtual resource as a candidate virtual resource for the request.

A system to facilitate infrastructure management is described. The system includes one or more processors and a non-transitory machine-readable medium storing instructions that, when executed, cause the one or more processors to execute an infrastructure management controller to receive first monitoring data indicating a first infrastructure condition occurring at an on-premise infrastructure controller, determine a first load state of the on-premise infrastructure controller based on the first infrastructure condition and adjust a consistency level of the on-premise infrastructure controller to a first level of the consistency based on the first state.

A method, an apparatus, a system, and an electronic device for selecting an intelligent analysis algorithm. The method includes: acquiring image data of a monitoring scene (S101); analyzing the image data to obtain scene contents contained in the image data (S102); determining an intelligent analysis algorithm corresponding to each of the scene contents (S103); and selecting a target intelligent analysis algorithm(s) from intelligent analysis algorithms corresponding to the scene contents according to a load capacity of a compute node used for loading the intelligent analysis algorithms, wherein a total algorithm load of the target intelligent analysis algorithm(s) is not greater than the load capacity of the compute node (S104). The method for selecting an intelligent analysis algorithm realizes an automatic selection of the intelligent analysis algorithm, which can reduce the manual workload, improve the selection efficiency of the intelligent analysis algorithm, reduce overload of the compute node, reduce abnormal analysis results caused by the overload of the compute node, and reduce an improper selection of the intelligent analysis algorithm due to the low degree of professionalism of the construction personnel, which affects the analysis effect.

An intended state of a computerised resource repository is computed based on a received data model, received historical data, and received error data is provided. The data model comprises rules for updating the state of each computerised resource repository of a plurality of computerised resource repositories based on one more inputs. The state of each computerised resource repository comprises a volume of the resource in said each computerised resource repository. The historical data comprises a plurality of previous inputs to the data model. The error data indicates of an error in one of the previous inputs of the historical data. A difference is then determined between the current volume of the resource in the computerised resource repository, indicated by received data, and the volume of the resource in the intended state of the computerised resource repository. Finally, a volume of the resource equal to the determined difference is allocated to the computerised resource repository from a control computerised resource repository, or vice versa, to correct the difference

A request to execute a workload that utilizes an amount of resources to be executed is received from a client device. Corresponding resources that are available at multiple non-uniform memory access (NUMA) nodes are received from one or more host systems. A particular NUMA node of the multiple NUMA nodes is identified in view of the particular NUMA node having available resources that are greater than the amount of resources to execute the workload. A scheduling hint is assigned to the workload that indicates that the particular NUMA node is to be used to execute the workload.

The present disclosure includes systems, methods, and computer-readable mediums for discovering capabilities of a hardware (HW) accelerator card. A processor may communicate a request for a listing of acceleration services to a HW accelerator card connected to the processor via the communication interface. The HW accelerator card may retrieve the listing from memory and provide a response to the processor that includes a listing of the HW acceleration services provided by the HW accelerator card.

A software defined (SD) process control system (SDCS) implements controller and other process control-related business logic as logical abstractions (e.g., application layer services executing in containers, VMs, etc.) decoupled from hardware and software computing platform resources. An SD networking layer of the SDCS utilizes process control-specific operating system support services to manage the usage of the computing platform resources and the creation, deletion, modifications, and networking of application layer services with devices disposed in the field environment and with other services, responsive to the requirements and needs of the business logic and dynamically changing conditions of SDCS hardware and/or software assets during run-time of the process plant (such as performance, faults, addition/deletion of hardware and/or software assets, etc.). Thus, dynamic (re-)allocation of hardware/software resources is primarily, if not entirely, and continually governed in real-time by present requirements and needs of application layer services as well as dynamically changing SDCS conditions.

A system and method is provided for dynamic renewable runtime resource management in response to flexible resource allocations by a processor. In embodiments, a method includes: calculating, by a processor of a system, a resource consumption value of a first workload by aggregating allocation values of persistent resources currently allocated to the first workload by the processor; determining, by the processor, that the resource consumption value of the first workload is greater than a predefined resource allocation target for the first workload; and temporarily adjusting, by the processor, a renewable runtime resource target of the first workload from an initial target value to a temporary target value based on the resource consumption value.

The present disclosure discloses a workflow scheduling method and system based on a multi-target particle swarm algorithm, and a storage medium. The method comprises the following steps that first, the difference between the frequency reduction characteristic and the execution time of each server in a cluster is considered; a multi-target comprehensive evaluation model covering workflow execution overhead, execution time and cluster load balance is constructed on the basis of a traditional model; second, a multi-target particle swarm algorithm is provided for workflow scheduling, and an efficient solving method is provided. The method alleviates the defects of premature convergence and low species diversity of the particle swarm algorithm, reduces the execution overhead and execution time of the workflow on the cluster server, and better balances the load of the cluster server.

Methods, apparatus, computer program products for resource scheduling are provided. The method comprises: receiving a workload request; publishing the information of the workload to a workload billboard accessible to a plurality of computer hosts, each of the plurality of computer hosts being associated with a corresponding proxy configured to manage the resource scheduling of the computer host; receiving a request to schedule at least a portion of the workload from a proxy; and sending the portion of the workload to the computer host associated with the proxy.