A mainframe computing system hosts a plurality of logical partitions, each having a static entitlement of processing capacity. The mainframe computer system has a workload manager that schedules work requested by the logical partitions and tracks consumption of the processing capacity by the logical partitions, and a capping policy that is stored in non-transitory memory and which identifies a subset of the logical partitions. The mainframe computer system further includes a capping master that is configured to allocate dynamically varying entitlements of processing capacity to the subset of the logical partitions based on the high-importance work percentages of computing workloads running on the logical partitions to encourage completion of high-importance work over completion of low-importance work. The capping master limits the allocated dynamic entitlement amount in millions of service units per hour (MSU) for each system usage entity to be no greater than the static entitlement of the system usage entity.

A device may include a processor. The processor may sample a portion of a workload to offload to compute resources and a portion of a current workload of the compute resources. The processor may simulate offloading of the workload to the compute resources using the sampled portion of the workload, the sampled portion of the current workload, and telemetry data corresponding to the compute resources. The compute resources may be configured to perform the workload according to a plurality of offloading configurations. The processor may determine a rank score for each offloading configuration of the plurality of offloading configurations based on the simulations. Responsive to a rank score corresponding to an offloading configuration of the plurality of offloading configurations exceeding a threshold value, the processor may offload the workload to the compute resource corresponding to the offloading configuration that corresponds to the rank score that exceeds the threshold value.

Disclosed methods include maintaining a database of resource limits for a plurality of agents. A resource limit may be usable for predicting a result of a given request from a given agent. Maintaining the database may include determining an updated resource limit for a particular agent based on identifying an extrema point of a function of resource limit. The maintaining may further include updating the database using the updated resource limit, as well as selecting, from the database, a subset of the plurality of agents that are selected based on associated parameter values compared to parameter values associated with the particular agent. The maintaining may also include updating corresponding resource limits for the subset of the plurality of agents based on the updated resource limit. The method may further include receiving a request from the particular agent, and predicting, using the updated resource limit, a result of the request.

An electronic device including an application processor and a communication processor. The communication processor, for managaging a resource memory, configured to monitor an occupancy rate of the resource memory for detecting a memory leakage, release a network connection when the electronic device is in an idle state, in response to the detecting of the memory leakage, initialize the resource memory after the network connection is released, and reconnect the network connection, wherein the idle state indicates a state when the electronic device is not occupied or used by a user, and wherein the initialization is performed at a non-flag area from among the resource memory.

A method, system, and apparatus determines whether a task should be relocated from a first processor to a second processor by comparing performance metrics to associated thresholds or by using other indications. The task is relocated from the first processor to the second processor and executed on the second processor based on the com paring.

A system for facilitating scalable shared rendering, including plurality of servers communicably coupled to each other, each server executing executable instance of rendering software, being communicably coupled to display apparatus(/es) , wherein when executed, rendering software causes each server to receive information indicative of poses of users of display apparatus(/es), utilise three-dimensional model(/s) of extended-reality environment to generate images from poses, send images to respective display apparatus(/es) for display, wherein at least one of plurality of servers is configured to detect when total number of display apparatuses to be served exceeds predefined threshold number, and employ new server and execute new executable instance of rendering software when predefined threshold number is exceeded, wherein new display apparatuses are served by new server, thereby facilitating scalable shared rendering.

Methods, apparatus, systems, and articles of manufacture are disclosed to improve workload domain management of virtualized server systems. An example system includes memory, programmable circuitry, and instructions to program the programmable circuitry to generate a pool of virtualized servers based on a policy, determine whether a utilization of a first virtualized server is less than a first threshold, the first threshold based on at least one type of resource provisionable to the first virtualized server, determine that a firmware status associated with the first virtualized server corresponds to a first firmware version, transfer a workload of the first virtualized server to a second virtualized server after a determination that a second firmware version is available for the first virtualized server, deallocate the first virtualized server from the first workload domain to the pool of the virtualized servers, and update the first virtualized server to the second firmware version.

Examples described herein include systems and methods for efficiently scaling an SDDC. An example method can include storing resource utilization information for a variety of resources of the SDDC. The example method can also include predicting a future resource utilization rate for the resources and determining that a predicted utilization rate is outside of a desired range. The system can determine how long it would take to perform the scaling, including adding or removing a host and performing related functions such as load balancing or data transfers. The system can also determine how long the scaling is predicted to benefit the SDDC to ensure that the benefit is sufficient to undergo the scaling operation. If the expected benefit is greater than the benefit threshold, the system can perform the scaling operation.

Aspects involve an apparatus, device, systems, and methods for instantiating and operating a cloudless infrastructure of computing devices that communicate peer-to-peer and mostly off-grid (or otherwise without communicating through a conventional centralized network) to share resources, access, and provide services and applications, store and access data and other information, and the like. The systems may provide services to connecting computing devices, such as user devices, personal computing devices, mobile devices, laptops, personal computers, Internet of Things (IoT) devices etc., in communication with one or more of the nodes of the infrastructure. The infrastructure exchanges or manages communications, transactions, and/or data in a cloudless and/or decentralized environment to freely exchange information between the nodes to allow the infrastructure to scale in response to client demands, adapt the infrastructure to a failed node with minimal impact on connected computing devices, and provide robust security to customer information, communications, and devices.

In one implementation, a system for policy based workload scaler includes a parameters engine to define external factors for a number of resources providing a number of cloud service workloads, a threshold engine to define a threshold value for the cloud service workloads from the number of resources, a priority engine to assign a priority to each of the number of cloud service workloads, and a service engine to reclaim resources from a first portion of cloud service workloads with a first priority and allocate the reclaimed resources to a second portion of cloud service workloads when the threshold value is exceeded and the external factors are exceeded.