Measuring quality-of-experience (QoE) for virtual reality (VR) streaming content is disclosed. A network computing device receives a client-side VR stream capture and a client pose data set that are generated by a client computing device based on a VR content and one or more induced network impairments (e.g., latency, packet loss, and/or jitter, as non-limiting examples). Using the same VR content and the client pose data set, the network computing device generates a source VR stream capture that is not subjected to the one or more induced network impairments. The network computing device performs a frame-by-frame comparison of the client-side VR stream capture and the source VR stream capture. Based on the frame-by-frame comparison, the network computing device generates a QoE metric that indicates a degree of degradation of the client-side VR stream capture relative to the source VR stream capture.

A method and system for providing a NS instance satisfying a requested availability of a NSI comprises obtaining at least one VNFD for a VNF composing the NS, the VNFD being associated with at least one absolute availability value guaranteed according to at least one DF; obtaining an availability value of NFVI on which the VNF is to be deployed; determining a minimum availability value for a NS instance of the NS; selecting a VNF DF and RM for the VNF DF such that the product of the absolute availability value of the VNF DF, taking into account the selected RM, and of the availability value of the NFVI is greater than or equal to the minimum availability value for the NS instance; and instantiating the NS instance by instantiating at least one VNF instance according to the at least one selected VNF DF and corresponding RM.

This application provides example methods, systems, and devices for performing charging on a network resource. One example method includes sending, by a network slice charging function device, a first message to a network slice management function device, where the first message includes an identifier of a network slice instance and filtering information, the filtering information indicates the network slice management function device to send a value that is of a network slice performance indicator of the network slice instance and that matches the filtering information, and the network slice performance indicator describes network performance of the network slice instance. The network slice charging function device can then receive a second message from the network slice management function device, where the second message includes the value. The network slice charging function device can then perform charging on the network slice instance based on the value.

Various systems and methods for implementing an edge computing system to realize 5G network slices with blockchain traceability for informed 5G service supply chain are disclosed. A system configured to track network slicing operations includes memory and processing circuitry configured to select a network slice instance (NSI) from a plurality of available NSIs based on an NSI type specified by a client node. The available NSIs uses virtualized network resources of a first network resource provider. The client node is associated with the selected NSI. The utilization of the network resources by the plurality of available NSIs is determined using an artificial intelligence (AI)-based network inferencing function. A ledger entry of associating the selected NSI with the client node is recorded in a distributed ledger, which further includes a second ledger entry indicating allocations of resource subsets to each of the NSIs based on the utilization.

Techniques for modifying data packet transmission characteristics by an intermediate node in a network are disclosed. An intermediate node in a data transmission network determines a current estimated transmission time for packets being transmitted from the source node to the intermediate node. The node analyzes a data packet to determine a Quality of Service (QoS) requirement for transmission of the first data packet. Based on the current estimated transmission time for packets being transmitted from the source node to the intermediate node and the QoS requirement for transmission of the first data packet, the intermediate node selects one or more transmission characteristics for forwarding the first data packet toward the destination node. The intermediate node transmits the packet toward the destination node in accordance with the one or more transmission characteristics.

Techniques for modifying data packet transmission characteristics by an intermediate node in a network are disclosed. An intermediate node in a data transmission network determines a current estimated transmission time for packets being transmitted from the source node to the intermediate node. The node analyzes a data packet to determine a Quality of Service (QoS) requirement for transmission of the first data packet. Based on the current estimated transmission time for packets being transmitted from the source node to the intermediate node and the QoS requirement for transmission of the first data packet, the intermediate node selects one or more transmission characteristics for forwarding the first data packet toward the destination node. The intermediate node transmits the packet toward the destination node in accordance with the one or more transmission characteristics.

A performance indicator monitoring system (PIMS) receives a plurality of operational measurements (OMs) for each performance indicator (PI), which may be stored on a first storage. A rate of access by a monitoring system is determined for the PI. The rate may be determined, for example, related to a counter of the PI. An enriched dataset related to a subset of the plurality of OMs is generated, upon determination that the selected PI rate of use is higher than a primary threshold value. The generated enriched dataset may be stored in a second storage. In some embodiments, a component of the PIMS, such as a processing unit, is determined. A usage level of the component is then determined, and the enriched dataset is generated if the usage level of the component is below a secondary threshold.

A method for subscriber tier plan adjustment including: monitoring traffic flow for one or more subscriber of a plurality of subscribers on an operator’s network; determining a bandwidth requirement for each of the one or more subscribers; determining a recommended tier plan for each subscriber based on the subscribers’ bandwidth requirement; and providing the recommended tier plan for each of the subscribers to a network operator. A system for subscriber tier plan adjustment including: a learning module configured to monitor traffic flow for one or more subscribers of a plurality of subscribers on a network and determine a bandwidth requirement of each of the one or more subscribers; an analysis module configured to determine a recommended tier plan for each of the subscribers based on each subscriber’s bandwidth requirement; and a notification module configured to provide the recommended tier plan for each subscriber.

A method for subscriber tier plan adjustment including: monitoring traffic flow for one or more subscriber of a plurality of subscribers on an operator’s network; determining a bandwidth requirement for each of the one or more subscribers; determining a recommended tier plan for each subscriber based on the subscribers’ bandwidth requirement; and providing the recommended tier plan for each of the subscribers to a network operator. A system for subscriber tier plan adjustment including: a learning module configured to monitor traffic flow for one or more subscribers of a plurality of subscribers on a network and determine a bandwidth requirement of each of the one or more subscribers; an analysis module configured to determine a recommended tier plan for each of the subscribers based on each subscriber’s bandwidth requirement; and a notification module configured to provide the recommended tier plan for each subscriber.

Techniques for generating and monitoring service level objectives (SLOs) are disclosed. The techniques include an SLO platform performing: storing a first SLO definition of a first SLO including a first error budget for a first metric associated with a first service; storing a second SLO definition of a second SLO including a second error budget for a second metric associated with a second service; obtaining first telemetry data from a first data source associated with the first service; obtaining second telemetry data from a second data source associated with the second service; monitoring the first SLO at least by computing the first metric based on the first telemetry data and evaluating the first metric against the first error budget; and monitoring the second SLO at least by computing the second metric based on the second telemetry data and evaluating the second metric against the second error budget.