An information handling system may include a processor; a memory; the processor to execute computer code of an evolved packet core to initiate a tiered communication network access policy by: detecting the connection of each of a plurality of endpoint devices to a communication network via one of a plurality of access points; and determining if a communication channel among a plurality of communication channels is available on the communication network for each of the endpoint devices based on a tier assigned to each of the endpoint devices; the processor to execute computer code of a telemetry data module to: receive telemetry data descriptive of the use characteristics of the endpoint devices; and execute a communication network machine learning algorithm using the telemetry data to generate a network prediction model; the processor to execute computer code of a reallocation module to: predict network resource use across the communication channels of the communication network based on the network prediction model and, with the reallocation module, reallocate endpoint devices based on the predicted network resource use and tier assigned to the endpoint devices.

Embodiments are generally directed apparatuses, methods, techniques and so forth determine an access level of operation based on an indication received via one or more network links from a pod management controller, and enable or disable a firmware update capability for a firmware device based on the access level of operation, the firmware update capability to change firmware for the firmware device. Embodiments may also include determining one or more configuration settings of a plurality of configuration settings to enable for configuration based on the access level of operation, and enable configuration of the one or more configuration settings.

A method for copying source data from a source server to a destination server, that includes initiating, by a source copy manager, a copy operation of the source data, where during the copy operation, the method further includes obtaining resource statistics, setting a data chunk size based on the resource statistics, copying a data chunk to a read queue, where the data chunk includes a portion of the source data, and where the data chunk has the data chunk size, and sending, from the read queue, the data chunk to a network device, and ending the copy operation.

A method for copying source data from a source server to a destination server, that includes initiating, by a source copy manager, a copy operation of the source data, where during the copy operation, the method further includes obtaining resource statistics, setting a data chunk size based on the resource statistics, copying a data chunk to a read queue, where the data chunk includes a portion of the source data, and where the data chunk has the data chunk size, and sending, from the read queue, the data chunk to a network device, and ending the copy operation.

System and methods for generating a deployment, such as a software architecture, using existing telecommunication resources, such as microservices, data sources, and/or communication channels. A plain language message is received that describes requirements of a desired deployment. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more existing telecommunication resources for use in the desired deployment. In some implementations, recommendations are generated using a machine learning model that generates relevance scores for each of multiple existing telecommunication resources. A selection is received from among the recommended telecommunication resources, and the desired deployment is generated using the selected telecommunication resources.

System and methods for generating a deployment, such as a software architecture, using existing telecommunication resources, such as microservices, data sources, and/or communication channels. A plain language message is received that describes requirements of a desired deployment. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more existing telecommunication resources for use in the desired deployment. In some implementations, recommendations are generated using a machine learning model that generates relevance scores for each of multiple existing telecommunication resources. A selection is received from among the recommended telecommunication resources, and the desired deployment is generated using the selected telecommunication resources.

A method for copying source data from a source server to a destination server, that includes initiating, by a source copy manager, a copy operation of the source data, where during the copy operation, the method further includes obtaining resource statistics, setting a data chunk size based on the resource statistics, copying a data chunk to a read queue, where the data chunk includes a portion of the source data, and where the data chunk has the data chunk size, and sending, from the read queue, the data chunk to a network device, and ending the copy operation.

A method for copying source data from a source server to a destination server, that includes initiating, by a source copy manager, a copy operation of the source data, where during the copy operation, the method further includes obtaining resource statistics, setting a data chunk size based on the resource statistics, copying a data chunk to a read queue, where the data chunk includes a portion of the source data, and where the data chunk has the data chunk size, and sending, from the read queue, the data chunk to a network device, and ending the copy operation.

There is disclosed in one example an application-specific integrated circuit (ASIC), including: an artificial intelligence (AI) circuit; and circuitry to: identify a flow, the flow including traffic diverted from a core cloud service of a network to be serviced by an edge node closer to an edge of the network than to the core of the network; receive telemetry related to the flow, the telemetry including fine-grained and flow-level network monitoring data for the flow; operate the AI circuit to predict, from the telemetry, a future service-level demand for the edge node; and cause a service parameter of the edge node to be tuned according to the prediction.

There is disclosed in one example an application-specific integrated circuit (ASIC), including: an artificial intelligence (AI) circuit; and circuitry to: identify a flow, the flow including traffic diverted from a core cloud service of a network to be serviced by an edge node closer to an edge of the network than to the core of the network; receive telemetry related to the flow, the telemetry including fine-grained and flow-level network monitoring data for the flow; operate the AI circuit to predict, from the telemetry, a future service-level demand for the edge node; and cause a service parameter of the edge node to be tuned according to the prediction.