Various embodiments disclosed herein are related to a non-transitory computer readable storage medium. In some embodiments, the medium includes instructions stored thereon that, when executed by a processor, cause the processor to receive, at a server, from a cluster of nodes on an edge network in communication with the server, a resource consumption data of a service hosted on the edge network, determine, based on a metering policy, a unit of measurement, and calculate a resource consumption quantity according to the unit of measurement.

A network services provider delivers bandwidth services to users in relation to a dynamic limit based on an average user consumption, rather than a fixed amount per subscriber. An average user consumption is employed to compute a consumption limit from relative portions of a user community substantially exceed the average, typically defined as “heavy users.” Usage levels below the consumption limit have an increased availability up to the consumption limit. The consumption limit may vary from a total capacity of the service infrastructure, which changes, generally in an increasing manner, as additional resources are added to the network. The consumption limits may be defined over different service intervals, such as daily, weekly or monthly, and for different groups of users depending on network layout.

A data collection server includes a communicator, a use frequency estimator, and a data saver. The communicator receives device data from a device. The use frequency estimator estimates use frequency of the device data received by the communicator. The data saver stores the device data in, among a plurality of storage servers corresponding to different levels of use frequency, one of the plurality of storage servers that corresponds to the use frequency estimated by the use frequency estimator.

In one example, the present disclosure describes a device, computer-readable medium, and method for proactively adjusting the infrastructure of a communications network in response to reporting of real-time network performance. For instance, in one example, a method includes obtaining real-time network performance metrics directly from a user endpoint device operated by a customer of a telecommunication service provider network, correlating the real-time network performance metrics with data from another data source, wherein the data includes data other than network performance metrics, and adjusting an infrastructure of the telecommunication service provider network in response to an insight gleaned through the correlating.

In one example, the present disclosure describes a device, computer-readable medium, and method for proactively adjusting the infrastructure of a communications network in response to reporting of real-time network performance. For instance, in one example, a method includes obtaining real-time network performance metrics directly from a user endpoint device operated by a customer of a telecommunication service provider network, correlating the real-time network performance metrics with data from another data source, wherein the data includes data other than network performance metrics, and adjusting an infrastructure of the telecommunication service provider network in response to an insight gleaned through the correlating.

A method and computer readable media are disclosed to supply values for either/both of volume/time for usage-monitoring from/to PCRF for a particular usage monitoring key. In one embodiment, the method includes identifying a monitoring key AVP used by a PCRF to provide at least one of a volume usage threshold level for a Policy and Charging Enforcement Function (PCEF) and a time of usage threshold level for a PCEF; wherein the monitoring key includes at least one of a volume threshold total for the monitoring key; a volume quota uplink threshold for the monitoring key; a volume quota downlink threshold for the monitoring key; and a time threshold for the monitoring key; and supplying values for at least one of volume for usage-monitoring for a PCRF for a particular usage monitoring key; and time for usage-monitoring for a PCRF for a particular usage monitoring key.

A session management function (SMF) entity related to a first protocol data unit (PDU) session between a user equipment (UE) and a data network (DN) for a network slice, for monitoring data usage is provided. The SMF entity includes a transceiver, and at least one processor coupled with the transceiver and configured to determine to change at least one of a target user plane function (UPF) or a target SMF for connectivity between the UE and the DN for the network slice, transmit an update request message including accumulated data usage information for the first PDU session to a policy control function (PCF) configured to update remaining allowed usage, based on the determining to change at least one of the target UPF or the target SMF, and receive a response message from the PCF, the remaining allowed usage being updated based on the accumulated data usage information.

Embodiments of the present invention relate to a centralized network analytic device, the centralized network analytic device efficiently uses on-chip memory to flexibly perform counting, traffic rate monitoring and flow sampling. The device includes a pool of memory that is shared by all cores and packet processing stages of each core. The counting, the monitoring and the sampling are all defined through software allowing for greater flexibility and efficient analytics in the device. In some embodiments, the device is a network switch.

A data collection server includes a communicator, a use frequency estimator, and a data saver. The communicator receives device data from a device. The use frequency estimator estimates use frequency of the device data received by the communicator. The data saver stores the device data in, among a plurality of storage servers corresponding to different levels of use frequency, one of the plurality of storage servers that corresponds to the use frequency estimated by the use frequency estimator.

An eNodeB or other cell access point device can receive demand data from mobile devices served by the cell. The demand data can represent an estimate of demand over a future period for network resources (e.g., bandwidth). The cell can aggregate this demand data and determine aggregation data for the time period or for various intervals of the time period, then transmit the aggregation data to the mobile devices. The aggregation data can operate as a collaborative approach to scheduling traffic. For example, data (e.g., delay tolerant data) can be shifted (e.g., delayed for a few seconds) based on an examination of the aggregation data in conjunction a determined priority of the data. Such can be applicable to data traffic not traditionally thought of as delay tolerant such as streaming video or web browsing, and can be accomplished without negatively impacting the quality of service or experience of the client.