Provided in the embodiments of the present disclosure are a resource determination and information sending method and device, a storage medium and a processor. The resource determination method includes: receiving configuration information, the configuration information carrying indication information for indicating information of a Physical Resource Block (PRB) that supports resource assignment for a terminal with a subcarrier Resource Unit (RU) as a minimum granularity; receiving information carrying a resource assignment field, a Resource Indication Value (RIV) of a specified field in the resource assignment field being used for indicating resource information assigned to the terminal; and determining, according to the indication information and the RIV, a resource assigned to the terminal.

Managing hybrid cloud resources by grouping at least a portion of the elements of a data set according to attribute sensitivity into a cluster of elements, computing a resource allocation impact of the cluster of elements, computing an information gain associated with the set of elements, and allocating cloud resources according to the resource allocation impact and information gain.

A system described herein may provide a technique for the dynamic selection of configurable resources in an environment that includes a hierarchical or otherwise differentiated arrangement of configurable resources. The environment may include, or may be implemented by, a Distributed Resource Network (“DRN”), which may include hardware or virtual resources that may be configured, including the instantiation of containers, virtual machines, Virtualized Network Functions (“VNFs”), or the like. The DRN may be hierarchical in that some resources of the DRN may provide services to, and/or may otherwise be accessible to, a greater quantity of elements of the DRN or some other network.

Systems and methods for performing self-contained posture assessment from within a protected portable-code workspace are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory having program instructions that, upon execution, cause the IHS to: transmit, from an orchestration service to a local agent, a workspace definition that references an application, where the application comprises a first portion of code provided by a developer and a second portion of code provided by the orchestration service; and receive, from a local agent at the orchestration service, a message in response to the execution of the second portion of code within a workspace instantiated based upon the workspace definition. The second portion of code may inspect the contents of the runtime memory of the workspace upon execution, for example, by performing a stack canary check, a hash analysis, a boundary check, and/or a memory scan.

Systems and methods adjust workspaces based on available hardware resource of an IHS (Information Handling System) by which a user operates a workspace supported by a remote orchestration service. A security context and a productivity context of the IHS are determined based on reported context information. A workspace definition for providing access to a managed resource is selected based on the security context and the productivity context. A notification specifies a hardware resource of the IHS that is not used by the workspace definition, such as a microphone or camera that has not been enabled for use by workspaces. A productivity improvement that results from the updated productivity context that includes use of the first hardware resource is determined. Based on the productivity improvement, an updated workspace definition is selected that includes use of the first hardware resource in providing access to the managed resource via the IHS.

A system and method for robotically arbitrating cloud computing services utilizes resource parameters, tolerance values, and client system requirements to configure a meta-orchestrator to select a validated compatible service from a service resource pool and employ an orchestrator to migrate a client system to the selected service and utilize block chain technology for logging transactions, storing metadata and data.

Systems and methods are described for enabling quantum computing at an edge node of a network. For example, a machine learning component residing on each of a plurality of edge nodes of the network may be implemented to distribute application processing by network location and processing type, including distribution among classical processing at a central cloud, classical processing at an edge node, and quantum processing at a quantum edge node including a quantum computing device. By distributing certain applications, such as latency-sensitive applications of a higher order of complexity, to an edge node, and particularly a quantum edge node, latency may be reduced and complex application code may be processed more quicky using quantum computations. For applications to be processed using quantum processing, the machine learning component may further identify qubits for the quantum processing and define containers based on the qubits for deployment by the quantum computing device.

Techniques are described herein that are capable of dynamically re-allocating computing resources while maintaining network connection(s). Applications of users are run in a computing unit. Computing resources are allocated among the applications based at least in part on dynamic demands of the applications for the computing resources and resource limits associated with the respective customers. In a first example, the computing resources are dynamically re-allocated among the applications, as a result of changing the resource limit of at least one customer, while maintaining at least one network connection between a client device of each customer and at least one respective application. In a second example, the computing resources are dynamically re-allocated among the applications, as a result of changing the resource limit of at least one customer, while maintaining at least one network connection between an interface and a client device of each customer.

There is provided a method for handling packets in a network. The method is performed by a first entity. The method is performed in response to receipt of a first packet, from a first network slice in the network, to be scheduled for a first service. A first priority value is assigned to the first packet by a provider of the first service. The method comprises setting (100) a second priority value for the first packet based on a comparison of the first priority value with at least one reference priority value. The second priority value is indicative of a priority with which the first packet is to be scheduled relative to at least one other packet.

Implementations are described herein for automatic deployment of function block application programs (FBAPs) across process automation nodes of a process automation system. In various implementations, one or more constraints associated with execution of a FBAP may be identified. Based on the one or more constraints, a process automation system that includes a plurality of process automation nodes may be analyzed. Based on the analysis, a subset of two or more process automation nodes on which to distributively deploy the FBAP may be selected from the plurality of processing node. In response to selecting the subset, the FBAP may be distributively deployed across the two or more process automation nodes of the subset.