The present disclosure relates to a method, a device, and a program product for managing a computing system. In a method, a current state and a plurality of historical states of a computing device in a computing system are acquired, the plurality of historical states respectively describing historical states of the computing device in the computing system at a plurality of historical time points. In response to determining that the current state matches a scheduling type for scheduling the computing device, the plurality of historical states are searched for a historical state matching the current state. A historical scheduling policy associated with the historical state is determined. Based on the historical scheduling policy, a computing task to be executed by the computing device is allocated to at least one other computing device in the computing system.

A method may include allocating, based on a first load requirement of a first tenant, a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement of a second tenant. The second bin may be packed by transferring, to the second bin, the first load requirement of the first tenant based on the transfer not exceeding the first threshold of the fixed capacity of the second bin. In response to the transfer exceeding the first threshold of the fixed capacity of the second bin, allocating a third bin to handle the first load requirement of the first tenant.

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.

Disclosed are various implementations of approaches for reassigning hosts between computing clusters. A computing cluster assigned to a first queue is identified. The first queue can include a first list of identifiers of computing clusters with insufficient resources for a respective workload. A host machine assigned to a second queue can then be identified. The second queue can include a second list of identifiers of host machines in an idle state. A command can then be sent to the host machine to migrate to the computing cluster. Finally, the host machine can be removed from the second queue.

A computer-implemented method for balancing workload among one or more locations is disclosed. The method may comprise: receiving data associated with a workload forecast for a first location and a second location, the data comprising a number of orders expected to be received for the first and second locations for a predetermined period of time; determining a first set of ratios of workload forecast for the locations relative to a first sum of the workload forecast for the first and second locations, the first set of ratios comprising at least a first forecast ratio for the first location and a second forecast ratio for the second location; receiving electronic orders for the predetermined period of time, the electronic orders comprising one or more groups of items and being assigned to one of the locations; and reassigning a first subset of electronic orders for the first location to the second location.

Techniques for managing resources of a virtualized system involve acquiring historical distribution data about a virtualized system, the historical distribution data indicating a historical distribution of resources occupied by workloads on a plurality of host machines of the virtualized system over a predetermined historical time period. The techniques further involve generating predicted distribution data based on the historical distribution data, the predicted distribution data indicating an estimated distribution of resources occupied by the workloads on the plurality of host machines over a predetermined future time period. The techniques further involve performing workload migration at least once based on the predicted distribution data, the workload migration including migrating a workload of a first host machine whose first estimated quantity of occupied resources exceeds a high threshold to a second host machine whose second estimated quantity of occupied resources is below a low threshold.

Examples described herein relate to a method and a system, for example, a workload controller, for accessing purged workloads. An alert indicative of an attempt to access a purged workload of workloads deployed in a workload environment may be received by the workload controller. The purged workload may include one or both of a deactivated workload or an archived workload. The workload controller may detect the attempt to access the purged workload based on port mirrored data traffic. Further, in some examples, the workload controller may activate the purged workload based on the alert.

A processor may identify a first plurality of transformation nodes from a machine learning pipeline. The processor may couple the first plurality of transformation nodes in series to obtain a sequence of transformation nodes. The processor may select a first transformation node and a second transformation node from the sequence of transformation nodes based on at least one of an input data size and output data size of each of the first plurality of transformation nodes, the second transformation node being subsequent and adjacent to the first transformation node in the sequence of transformation nodes. The processor may obtain an optimized machine learning pipeline by coupling a second plurality of transformation nodes from the machine learning pipeline between the first transformation node and the second transformation node in the sequence of transformation nodes.

Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Systems and methods are provided for offloading a task from a central processor in a radio access network (RAN) server to one or more heterogeneous accelerators. For example, a task associated with one or more operational partitions (or a service application) associated with processing data traffic in the RAN is dynamically allocated for offloading from the central processor based on workload status information. One or more accelerators are dynamically allocated for executing the task, where the accelerators may be heterogeneous and may not comprise pre-programming for executing the task. The disclosed technology further enables generating specific application programs for execution on the respective heterogeneous accelerators based on a single set of program instructions. The methods automatically generate the specific application programs by identifying common functional blocks for processing data traffic and mapping the functional blocks to the single set of program instructions to generate code native to the respective accelerators.