Provided are a resource pool management system, a resource pool management method, and a program which are capable of effectively utilizing hardware resources in which various functional units that achieve network services are deployed. An E2EO module identifies, when a specific type of functional unit is deployed on an unused hardware resource that is not included in any of a plurality of resource pools, the resource pool linked to the specific type of functional unit. A CMaaS module and a BMaaS module perform a system software setup in accordance with the specific type of functional unit on the unused hardware resource. The CMaaS module and the BMaaS module update resource pool management data to add the unused hardware resource on which the system software setup has been performed to the identified resource pool.

Central processing units (CPUs) are configured to support host access instruction(s) that are associated with accessing solid state storage. A resource management module, implemented independently of the CPUs, receives a resource allocation request that includes a usage type identifier and requested amount of a resource, where the usage type identifier is associated with a group identifier. Adjustable resource configuration information is accessed to obtain: (1) a maximum associated with the usage type identifier, (2) a minimum associated with the usage type identifier, and (3) a group limit associated with the group identifier. Resource state information is accessed and it is determine whether to grant the request based at least in part on the maximum, minimum, group limit, and resource state information. The resource allocation request is then granted or denied based on the determination.

In a computing environment, a set of executing processes each having associated resources are provided. Aggregate resources for the computing environment include multiple different types of resources. A utilization level for each of the resources within the computing environment is evaluated to determine an unconsumed capacity for each of the resources below a utilization threshold. The utilization threshold is resource-dependent. An indication of at least a portion of unconsumed capacity for each of the resources below the utilization threshold is gathered. The unconsumed portion for each of the resources below the utilization threshold is exposed for consumption by other executing processes.

Examples described herein relate to method, resource management system, and non-transitory machine-readable medium for redeploying a computing resource. Data related to a performance parameter corresponding to a plurality of computing resources deployed on a plurality of host-computing nodes may be received. The performance parameter is associated with one or both of: communication between computing resources of the plurality of computing resources, or communication of the plurality of computing resources with a network device. Further, for a computing resource of the plurality of computing resources, a candidate host-computing node is determined from the plurality of host-computing nodes based on the data related to the performance parameter and the computing resource may be redeployed on the candidate host-computing node.

Virtual application and desktop delivery may be optimized by supplying application metadata and user intent to the device between a client and a server hosting resources for the delivery. The data packets used to deliver the virtual application or desktop may be also tagged with references to the application. By supplying the metadata and tagging packets with the metadata, an intermediary network device may provide streams of data packets at the target QoS. In addition, the device may apply network resource allocation rules (e.g., firewalls and QoS configuration) for redirected content retrieved by the client out of band relative to a virtual channel such as the Internet. The network resource allocation rules may differ for different types of resources accessed. The device may also control a delivery agent on the server to modify communication sessions established through the virtual channels based on network conditions.

A computing node to implement an RL management entity in an NG wireless network includes a NIC and processing circuitry coupled to the NIC. The processing circuitry is configured to generate a plurality of network measurements for a corresponding plurality of network functions. The functions are configured as a plurality of ML models forming a multi-level hierarchy. Control signaling from an ML model of the plurality is decoded, the ML model being at a predetermined level (e.g., a lowest level) in the hierarchy. The control signaling is responsive to a corresponding network measurement and at least second control signaling from a second ML model at a level that is higher than the predetermined level. A plurality of reward functions is generated for training the ML models, based on the control signaling from the MLO model at the predetermined level in the multi-level hierarchy.

A computer-implemented for allocating resources is disclosed. The method includes: receiving, from a client device associated with an entity, input including a selection of a first operation; obtaining a threshold quantity of resources associated with the first operation; allocating a first quantity of resources associated with the entity to the first operation; detecting a trigger condition for obtaining resources associated with the first operation; and in response to detecting the trigger condition for obtaining resources associated with the first operation: determining a second quantity of resources associated with the entity for allocation to the first operation based on a difference between the threshold quantity of resources associated with the first operation and the first quantity of resources; and transmitting, to the client device, a signal representing a message indicating the second quantity of resources.

Techniques for tenant-driven dynamic resource allocation in network functions virtualization infrastructure (NFVI). In one example, an orchestration system is operated by a data center provider for a data center and that orchestration system comprises processing circuitry coupled to a memory; logic stored in the memory and configured for execution by the processing circuitry, wherein the logic is operative to: compute an aggregate bandwidth for a plurality of flows associated with a tenant of the data center provider and processed by a virtual network function, assigned to the tenant, executing on a server of the data center; and modify, based on the aggregate bandwidth, an allocation of compute resources of the server executing the virtual network function.

Bandwidth throttling in a browser isolation environment is disclosed. A request is received from a client browser executing on a client device to connect with a remote resource. The browser isolation system provides a surrogate browser to facilitate communications between the client browser and the remote resource. A throttle is applied to a portion of content delivered to the client browser in response to the received request.

Disclosed herein are guidance/supervision systems that may utilize guidance and supervision policies from various stakeholders, such as resource owner(s) and service owner(s), to provide guidance, supervision, and introspective analysis for an autonomous system that configures a group of shared resources. The system includes determining, based on predefined preferences associated with shared resources, a set of policies to guide an autonomous system in determining a set of configuration parameters for sharing the shared resources. The system also includes providing the set of policies to the autonomous system to configure the shared resources according to the set of configuration parameters.