Various methods and systems for implementing request scheduling and processing in a multi-tenant distributed computing environment are provided. Requests to utilize system resources in the distributed computing environment are stored in account queues corresponding to tenant accounts. If storing a request in an account queue would exceed a throttling threshold such as a limit on the number of requests stored per account, the request is dropped to a throttling queue. A scheduler prioritizes processing requests stored in the processing queue before processing requests stored in the account queues. The account queues can be drained using dominant resource scheduling. In some embodiments, a request is not picked up from an account queue if processing the request would exceed a predefined hard limit on system resource utilization for the corresponding tenant account. In some embodiments, the hard limit is defined as a percentage of threads the system has to process requests.

Core network slices that belong to a given operator community are efficiently tracked at the network control/user plane functions level, with rich data analytics in real-time based on their geographic instantiations. In one aspect, an enhanced vendor agnostic orchestration mechanism is utilized to connect a unified management layer with an integrated slice-components data analytics engine (SDAE), a slice performance engine (SPE), and a network slice selection function (NSSF) in a closed-loop feedback system with the serving network functions of one or more core network slices. The tight-knit orchestration mechanism provides economies of scale to mobile carriers in optimal deployment and utilization of their critical core network resources while serving their customers with superior quality.

A method for facilitating operations in storage facilities. A server receives a service request for performing a first operation in the storage facility. The server identifies a first storage unit based on the service request. The server identifies an operation zone of the first storage unit for performing the first operation. The server determines an ergonomic score for each operator for performing the first operation based on characteristics of the operation zone and the fatigue level of the corresponding operator. The server allocates the first storage unit to a first operator for performing the first operation, based on the determined ergonomic scores, thereby ensuring an increased throughput of the storage facility.

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 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.

A network device may receive configuration data identifying resource subscription thresholds associated with a plurality of respective endpoint devices and may receive traffic from the plurality of endpoint devices. The network device may process the traffic and the configuration data, with a resource allocation model, to determine that processing traffic associated with a first endpoint device requires allocating a resource of the network device, and may process the configuration data, with the resource allocation model, to identify the resource of the network device from a particular resource of the network device that is currently allocated to traffic associated with a second endpoint device. The network device may allocate the particular resource of the network device to the traffic associated with the first endpoint device, and may process the traffic associated with the first endpoint device with the particular resource to generate processed traffic.

A method, device, and system for ensuring a service level agreement (SLA) of an application, where the method includes: obtaining, by an application function (AF) entity, information about a first network slice instance (NSI) that is in network slice instances between a specified location and a target network and whose SLA support capability meets a subscribed SLA requirement of the application, and sending a notification message including the information about the first NSI, where the notification message includes the information about the first NSI, to establish a new session in the first NSI for a terminal.

A method, a device, and a non-transitory storage medium are described in which multi-tiered networks and resource utilization-based provisioning service is provided. A multi-tiered mobile edge computing network that includes multiple mobile edge computing networks that are multi-tiered based on distance from a network edge includes a network device that selects a location to provision an application service for an end device based on a total resource utilization value and a performance metric associated with one or multiple candidate mobile edge computing networks.

This disclosure describes techniques for collecting network parameter data for network switches and/or physical servers and provisioning virtual resources of a service on physical servers based on network resource availability. The network parameter data may include network resource availability data, diagnostic constraint data, traffic flow data, etc. The techniques include determining network switches that have an availability of network resources to support a virtual resource on a connected physical server. A scheduler may deploy virtual machines to particular servers based on the network parameter data in lieu of, or in addition to, the server utilization data of the physical servers (e.g., CPU usage, memory usage, etc.). In this way, a virtual resource may be deployed to a physical server that has an availability of the server resources, but also is connected to a network switch with the availability of network resources to support the virtual resource.