Flexible computation capacity orchestration can include obtaining, at a computer, operational data from an edge device that can communicate with the computer via a network. The operational data can include utilization data that can define a resource utilization of the edge device. If a determination is made that the resource utilization of the edge device satisfies the upper utilization limit, a command can be issued to create a device group that comprises the edge device and a further edge device. Operational data can be obtained from the edge device and the further edge device, the operational data defining a further utilization of the edge device and a utilization of the further edge device. If a determination is made that the further utilization is below the lower utilization limit, a further command to end the device group can be issued.

A solution is proposed for controlling invocations of a target component by multiple source components in a software application. A corresponding method comprises associating a plurality of source components in a software application with one or more corresponding source rates for invoking a target component in the software application; monitoring corresponding invocations of the target component by a number of instances of the plurality of source components; receiving an enablement request for a new invocation of the target component from a current instance of a current source component; verifying an enablement of the new invocation; estimating a serving probability indicative of a residual capability of the target component to serve the new invocation; and enabling the new invocation according to the serving probability.

Techniques for managing critical workloads in container-based computing environments are disclosed. In one example, a method determines a resource trigger threshold associated with executing at least one containerized workload associated with a first service having a first criticality level, the resource trigger threshold corresponding to a resource capacity allocated to execute the first service. The method determines when the resource capacity allocated to execute the first service reaches the resource trigger threshold, and then re-allocates resource capacity allocated to execute at least one containerized workload associated with a second service having a second criticality level to the first service when the resource trigger threshold is reached. For example, the first criticality level may be higher than the second criticality level.

Systems and methods scale an instance group of a computing platform by determining whether to scale up or down the instance group by using historical data from prior jobs wherein the historical data includes one or more of: a data set size used in a prior related job and a code version for a prior related job. The systems and methods also scale the instance group up or down based on the determination. In some examples, systems and methods scale an instance group of a computing platform by determining a job dependency tree for a plurality of related jobs, determining runtime data for each of the jobs in the dependency tree and scaling up or down the instance group based on the determined runtime data.

In some examples, a computing system receives an indication of an increased workload portion to be added to a workload of a storage system, the workload comprising buckets of operations of different characteristics. The computing system computes, based on quantities of operations of the different characteristics in the workload, factor values that indicate distribution of operations of the increased workload portion to the buckets of operations of the different characteristics, and distributes, according to the factor values, the operations of the increased workload portion into the buckets of operations of the different characteristics.

A method of configuring a cloud server resource, an electronic device and a storage medium, which specifically relate to a field of artificial intelligence technology, in particular to a field of cloud computing technology. A method of configuring a cloud server resource includes: obtaining an operation index of a server instance of an object; comparing the obtained operation index with a threshold; and determining whether a configuration of the cloud server resource is to be adjusted, based on a result of the comparing.

A computer-implemented method according to one embodiment includes identifying a job creation request within a system; determining a current amount of available resources within the system; and conditionally deploying a reusable group of containers for the job, based on the current amount of available resources for the system.

Disclosed techniques relate to circuitry configured to aggregate and report usage information in a distributed processor (e.g., a GPU). In some embodiments, graphics processor circuitry that includes at least first and second portions that are respectively configured to execute sets of graphics work. First utilization circuitry may track execution time for sets of graphics work on the first portion of the graphics processor circuitry and second utilization circuitry may track execution time for sets of graphics work on the second portion of the graphics processor circuitry. Command queue circuitry may store multiple different command queues. Control circuitry may access the first and second utilization circuitry and aggregate utilization data on a per-command-queue basis, where for a given command queue, the aggregated utilization data indicates respective utilization of the first and second portions of the graphics processor circuitry. The control circuitry may provide the aggregated per-command-queue utilization data in software-accessible registers.

A datacenter includes a datacenter efficiency management system coupled to node devices. For each of the node devices and based on a power consumption associated with that node device and a performance associated with that node device, the datacenter efficiency management system generates a node group ranking that it uses to group subsets of the node devices into respective homogenous node groups, and then deploys a respective workload on at least one node device in each of the homogenous node groups. Based on at least one of a node workload bandwidth, a node power consumption, and a node health of each node device on which a workload was deployed, the datacenter efficiency management system then generates a workload performance efficiency ranking of the node devices that it then uses to migrate at least one workload between the node devices.

A network of systems used for tracking of performance of resources and components thereof using resource information. The resource information may include resource performance information (e.g., operation of the resource or components thereof, or the like) that is stored within a distributed computing network. The resource performance information may be compared to resource thresholds and used to determine and distribute resource suggestions for the resource. The distributed computing network may comprise a plurality of nodes that host a distributed register for storing, updating, and allowing access to resources and resource performance information, resource thresholds, and resource suggestions.