A container scheduling method and apparatus, and a computer-readable storage medium, which relate to the technical field of computers. The method includes: according to a resource usage amount of a container set copy which has run, determining a predicted resource usage amount of a container set copy to be scheduled, wherein the type of container set copy which has run is the same as the type of container set copy to be scheduled; according to the predicted resource usage amount and a resource supply amount supported by each candidate node, determining a candidate node matching the container set copy which has run; and scheduling the container set copy which has run to the matched candidate node for running.

Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

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 data processing method, a data processing apparatus, a distributed data flow programming framework, an electronic device, and a storage medium. The data processing method includes: dividing a data processing task into a plurality of data processing subtasks (S101); determining, in a Field Programmable Gate Array (FPGA) accelerator side, a target FPGA acceleration board corresponding to each of the data processing subtasks (S102); and sending data to be computed to the target FPGA acceleration board, and executing the corresponding data processing subtask by use of each of the target FPGA acceleration boards to obtain a data processing result (S103). According to the method, a physical limitation of host interfaces on the number of FPGA acceleration boards in an FPGA accelerator side may be avoided, thereby improving the data processing efficiency.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for the generation of a recommendation for one or more resource transformation actions to be performed based at least in part on an optimized resource transformation scenario. The optimized resource transformation scenario can be identified based at least in part on a hybrid resource transformation scenario that can be based at least in part on a resource priority score for a residual resource and a downgrade-only resource transformation scenario. The downgrade set of a plurality of resources can be determined based at least in part on resource transformation data associated with the plurality of resources.

Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

Recompiling code based on sustainability. Code is recompiled in a manner that accounts for sustainability values. When a deployment request is received, sustainability values are identified. The resources needed to fulfill the deployment request are identified based on the sustainability values and available resources. Once the resources that are likely to best meet the sustainability values are identified, the code is recompiled accordingly.

Techniques described herein relate to a method for allocating graphics processing unit partitions for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial graphics processing unit (GPU) partition allocation request associated with a CV workload; in response to obtaining the initial GPU partition allocation request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the GPU partition allocation request; generating an optimal GPU partition allocation based on the first CV environment configuration information and the CV workload information using a GPU partition model; and initiating performance of the CV workload in a CV environment based on the optimal GPU partition allocation.

Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; generating a CV alert case associated with the CV alert; identifying nearby CV nodes of the plurality of CV nodes; transmitting CV alert to the nearby CV nodes; for each of the nearby CV nodes: receiving the CV alert; determining, based on CV environment configuration information of the nearby CV node and the CV alert, whether to perform a distributed CV workload; when the determination is to perform the distributed CV workload: initiating performance of the distributed CV workload by the nearby CV nodes to generate CV data; updating the CV alert case using CV data generated during the performance of the distributed CV workload to obtain an updated CV alert case; and transmitting by the nearby CV node to the VMS the updated CV alert case.

Techniques for managing containerized workloads in a container computing environment are disclosed. For example, a method comprises the following steps. In a first mode, the method learns resources and execution times needed to process incoming workloads of a first workload type and a second workload type in a set of one or more clusters in a container-based computing environment. In a second mode, based on the learning of resources and execution times in the first mode, the method determines whether a subsequent incoming workload of the second workload type can be executed by one of the set of one or more clusters or whether an additional cluster should be created to process the subsequent incoming workload and then removed after processing the subsequent incoming workload is completed.