Resource management includes storing, for multiple workload pools of a data intake and query system, a workload pool hierarchy arranged in multiple workload pool layers. After storing a processing request is assigned a selected subset of workload pools in a second layer of the workload pool hierarchy based on a type of processing request. The processing request is then assigned to an individual workload pool in the selected subset to obtain a selected workload pool. Execution of the processing request is initiated on the selected workload pool.

Detecting non-callable external component APIs is provided. It is determined whether a first function call stack of a worker thread in a multi-threaded application of the computer matches a second function call stack of the worker thread. In response to determining that the first function call stack matches the second function call stack of the worker thread, an external component application programming interface (API) corresponding to the worker thread is identified from a function call stack of the worker thread. The external component API corresponding to the worker thread is marked as non-callable in an API state map. The worker thread is marked as being in a hang state. The worker thread in the hang state is terminated as a remediation action step to maintain performance.

Scalable I/O Virtualization (Scalable IOV) allows efficient and scalable sharing of Input/Output (I/O) devices across a large number of containers or Virtual Machines. Scalable IOV defines the granularity of sharing of a device as an Assignable Device Interface (ADI). In response to a request for a virtual device composition, an ADI is selected based on affinity to the same NUMA node as the running virtual machine, utilization metrics for the Input-Output Memory Management Unit (IOMMU) unit and utilization metrics of a device of a same device class. Selecting the ADI based on locality and utilization metrics reduces latency and increases throughput for a virtual machine running critical or real-time workloads.

A computing device, comprising: a computing module, comprising one or more computing units; and a control module, comprising a computing control unit, and used for controlling shutdown of the computing unit of the computing module according to a determining condition. Also provided is a computing method. The computing device and method have the advantages of low power consumption and high flexibility, and can be combined with the upgrading mode of software, thereby further increasing the computing speed, reducing the computing amount, and reducing the computing power consumption of an accelerator.

A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

A system including: one or more processors; a memory storing computer program code that controls the one or more processors to: receive usage metrics associated with a first application; determine whether the first application comprises a cyclic usage pattern, a batch usage pattern, or a non-cyclic usage pattern; select a scaling technique based on the determination; and automatically scale the first application with the selected scaling technique. The system may determine that at least one virtual machine should be added to a first plurality of virtual machines in response to a resource usage of an application exceeding a maximum usage allocation and determine that at least one virtual machine should be removed to the first plurality of virtual machines in response to a minimum usage allocation exceeding the resource usage of the first plurality of virtual machine instances.

Embodiments of the present invention provide a system for minimizing computational processing for converting user resources to resources supported by third party entities. In particular, the system may be configured to determine that a user has scanned a code projected on an entity device via a third party application present on a user device of the user, wherein the entity device is associated with an entity, establish a first connection with the entity device, establish a second connection between the user device and the entity device based on determining that the user has scanned the code, determine that the user has inserted user resources into the entity device, via the first connection, convert the user resources to resources supported by a third party entity, and display in real-time, information associated with the resources on the third party application.

Job execution environment control techniques are described to manage policy selection and implementation to control use of job executors by a computing device, automatically and without user intervention. These techniques are usable to select a policy from a plurality of policies that is then used to control lifecycles of job executors of a job execution environment of a computing device. Further, these techniques are usable to respond dynamically to change the selected policy during runtime of the application in response to changes in the job execution environment.

In one embodiment, a system includes first host machines implementing a public-cloud computing environment, wherein at least one of the first host machines comprises a resource manager that provides a public-cloud resource interface through which one or more public-cloud clients interact with one or more virtual machines, and second host machines implementing a private-cloud computing environment, wherein at least one of the second host machines comprises one or more private-cloud virtual machines, wherein at least one of the first host machines further comprises a private-cloud VM resource provider through which the resource manager interacts with the private-cloud virtual machines, wherein the VM resource provider translates requests to perform virtual machine operations from a public-cloud-resource interface to a private-cloud virtual machine interface, and the private-cloud virtual machines perform the requested virtual machine operations in response to receiving the translated requests from the VM resource provider.

A memory tier is established in a cluster system having a deduplicated file system. The memory tier includes memory pages configured as huge pages, where writes to the huge pages are exported in a device file that is outside of a user process namespace within which processes of the deduplicated file system run. At least a portion of metadata generated by the deduplicated file system is written to the memory tier. The portion of metadata includes an index of fingerprints corresponding to data segments stored by the deduplicated file system to a storage pool. A determination is made that an instance of the deduplicated file system has failed. A new instance of the deduplicated file system is started to recover file system services by loading the index of fingerprints from the device file.