Abstract: A system and method of dynamically controlling a reservation of resources within a cluster environment to maximize a response time are disclosed. The method embodiment of the invention includes receiving from a requestor a request for a reservation of resources in the cluster environment, reserving a first group of resources, evaluating resources within the cluster environment to determine if the response time can be improved and if the response time can be improved, then canceling the reservation for the first group of resources and reserving a second group of resources to process the request at the improved response time.

Abstract: Implementations detailed herein include description of a computer-implemented method.

Abstract: Disclosed system specifies, based on measurement results of communication times taken for accessing a plurality of external databases, relation between the communication times taken for accessing the plurality of external databases, calculates, when accepting an instruction to execute processing using at least one of the plurality of external databases, a processing load when accessing the at least one of the external databases, based on the relation between the communication times, and controls an access to data included in the at least one of the external databases according to the calculated processing load.

Abstract: A computer-implemented method, a computer program product, and a computer processing system are provided for selecting from among multiple Graphics Processing Unit (GPU) execution modes for a Neural Network (NN) having a size greater than a threshold size. The multiple GPU execution modes include a normal memory mode, an Out-of-Core (OoC) execution mode, and a Unified Memory (UM) mode. The method includes starting an execution on the NN with the UM mode and measuring the memory usage for each of layers of the NN. The method further includes selecting an execution mode based on the memory usage of all of the layers.

Abstract: At least one processing device comprises a processor and a memory coupled to the processor. The at least one processing device is configured to establish one or more groups of synchronization objects in a storage system based at least in part on object type, and for each of the one or more groups, to insert entries into a corresponding object type queue for respective objects of the group, to execute a monitor thread for the group, the monitor thread being configured to scan the entries of the corresponding object type queue, and responsive to at least one of the scanned entries meeting one or more designated conditions, to take at least one automated action for its associated object. The synchronization objects illustratively comprise respective locks, or other objects. The at least one processing device illustratively comprises at least a subset of a plurality of processing cores of the storage system.

Abstract: Some embodiments are directed to a logging within a software application executed over an assembly of information processing devices. More particularly, some embodiments relate to a method allowing process logging in the case of a software application operating with several processes and/or threads.

Abstract: Example techniques for execution of functions by clusters of computing nodes are described. In an example, if a cluster does not have resources available for executing a function for handling a service request, the cluster may request another cluster for executing the function. A result of execution of the function may be received by the cluster and used for handling the service request.

Abstract: Client application (112) submits request (118) to resource status service (110) for resource status data (“data”) regarding one or more computing resources (108) provided in a service provider network (102). The resource status service submits requests to the resources for the data. The resource status service provides a reply to the client application that includes any data received from the resources within a specified time. If all requested data was not received from the resources within the specified time the resource status service can also provide, in the reply, an identifier (“ID”) that identifies the request and can be utilized to identify and retrieve additional status data received at a later time. The client application can also submit additional requests for the status data, and may include the ID, may wait for additional data to be pushed to it, or may check a queue for the status data.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: An arithmetic processor performs arithmetic processing, and a synchronization processor, including first registers, performs synchronization processing that includes a plurality of processing stages to be processed stepwise. The arithmetic processor sends, to the synchronization processor, setting information to be used in a predetermined processing stage of the synchronization processing, and instruct the synchronization processor to execute the predetermined processing stage for the arithmetic processing. Each of the first registers includes a setting information management area to manage the setting information received from the arithmetic processor, and a destination status area to store a usage state of each of destination registers which are used in a next processing stage following the predetermined processing stage.

Abstract: Disclosed are aspects of workload selection and placement in systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some aspects, workloads are assigned to virtual graphics processing unit (vGPU)-enabled graphics processing units (GPUs) based on a variety of vGPU placement models. A number of vGPU placement neural networks are trained to maximize a composite efficiency metric based on workload data and GPU data for the plurality of vGPU placement models. A combined neural network selector is generated using the vGPU placement neural networks, and utilized to assign a workload to a vGPU-enabled GPU.

Abstract: A method includes receiving a request to migrate a virtual machine from a source host to a destination host, mapping, by a hypervisor running on the source host, a first portion of a memory of the virtual machine to a persistent memory device, where the persistent memory device is accessible by the source host machine and the destination host machine, responsive to determining that a time period to execute a synchronization operation with respect to the first portion of the memory by the persistent memory device is below a threshold, stopping the virtual machine on the source host, and starting the virtual machine on the destination host.

Abstract: A technique is described for managing processor (CPU) resources in a host having virtual machines (VMs) executed thereon. A target size of a VM is determined based on its demand and CPU entitlement. If the VM's current size exceeds the target size, the technique dynamically changes the size of a VM in the host by increasing or decreasing the number of virtual CPUs available to the VM. To “deactivate” virtual CPUs, a high-priority balloon thread is launched and pinned to one of the virtual CPUs targeted for deactivation, and the underlying hypervisor deschedules execution of the virtual CPU accordingly. To “activate” virtual CPUs, the number of virtual CPUs, the launched balloon thread may be killed.

Abstract: A memory device includes a plurality of memory dies, each memory die of the plurality of memory dies comprising a memory array and control logic. The control logic comprises a plurality of processing threads to execute memory access operations on the memory array concurrently, a thread selection component to identify one or more processing threads of the plurality of processing threads for a power management cycle of the associated memory die and a power management component to determine an amount of power associated with the one or more processing threads and request the amount of power during the power management cycle.

Abstract: The present disclosure relates to a method and system for optimizing a parameter configuration of a distributed computing job.

Abstract: Systems and methods are provided for managing dynamic controls over access to computer resources and, even more particularly, for evaluating and re-evaluating dynamic conditions and changes associated with user sessions. The systems and methods are configured to automatically make a determination as to whether new or additional authentication credentials are required for a user that is already authorized for accessing resources in a user session, in response to triggering events such as the identification of a new or changed condition associated with the user session.

Abstract: An output rule specified via a distributed system execution request data structure for a requested calculation is determined, and a current rule is initialized to the output rule. A rule lookup table data structure is queried to determine a set of matching rules, corresponding to the current rule. The best matching rule is selected. A logical dependency graph (LDG) data structure is generated by adding LDG nodes and LDG edges corresponding to the best matching rule, precedent rules of the best matching rule, and precedent rules of each precedent rule. An execution complexity gauge value and a set of distributed worker processes are determined. The LDG data structure is divided into a set of subgraphs. Each worker process is initialized with the subgraph assigned to it. Execution of the requested calculation is coordinated and a computation result of the LDG node corresponding to the output rule is obtained.

Abstract: Various examples are disclosed for performing cross-cluster load balancing. In some aspects, a workload is selected for cross-cluster migration. A destination cluster is identified for a migration of the workload from a source cluster to the destination cluster. A cross-cluster migration recommendation is generated to migrate the workload from the source cluster to the destination cluster.

Abstract: A method and system for collaborative workload placement and optimization. Specifically, the disclosed method and system entail implementing metric exchange between hosts in a clustered or non-clustered environment to perform infrastructure workload placement and optimization decisions. The exchange of metrics may be performed through a lightweight service, which would not impact any existing environment resource utilization. Further, resource utilization maps may be used to offer near real-time tracking of resource utilization across the aforementioned hosts, thereby facilitating resource utilization forecasting for the efficient placement and optimization of workloads throughout the environment.

Abstract: Aspects of the disclosure relate to determining relevant content in response to a request for information. One or more computing devices (170) may load data elements into registers (385A-385B), wherein each register is associated with at least one parallel processor in a group of parallel processors (380A-380B). For each of the parallel processors, the data elements loaded in its associated registers may be sorted, in parallel, in descending order. The sorted data elements, for each of the parallel processors, may be merged with the sorted data elements of other processors in the group. The merged and sorted data elements may be transposed and stored.