Radio access network intelligent application manager

Patent metadata

Field	Value
Publication number	US-2024259879-A1
Application number	US-202218563085-A
Country	US
Kind code	A1
Filing date	Nov 18, 2022
Priority date	Nov 19, 2021
Publication date	Aug 1, 2024
Grant date	—

Abstract

The present disclosure is related to edge and cloud computing frameworks, telemetry and telemetering systems, telemetry awareness and intelligence in managing telemetering systems, and Radio Access Network (RAN) and RAN intelligent controller (RIC) implementations. In particular, the present disclosure provides RIC-based resource management for individual RIC applications, which is based on the collection and analysis of platform telemetry data as well as measurements collected by user equipment and access network infrastructure elements.

First claim

1 - 53 . (canceled)

54 . An edge compute node, comprising:
memory circuitry configured to store instructions for operating an application (app) manager and a set of edge apps; and processor circuitry connected to the memory circuitry, wherein the processor circuitry is configured to operate the app manager to:
receive, via network interface circuitry (NIC), measurement data from a set of network access nodes (NANs) connected to the edge compute node;
receive, via the NIC, telemetry data from one or more telemetry agents implemented by the edge compute node;
determine a resource allocation for a corresponding edge app of the set of edge apps based on the measurement data and the telemetry data; and
configure at least one NAN of the set of NANs or the edge compute node according to the determined resource allocation such that resources indicated by the resource allocation are allocated to the corresponding edge app.

55 . The edge compute node of claim 54 , wherein the processor circuitry is configured to:
receive, via the NIC, a policy from a orchestration function, wherein the information included in the policy includes a set of key performance measurements (KPMs), key performance indicators (KPIs), service level agreement (SLA) requirements, or quality of service (QoS) requirements related to related to one or more of accessibility, availability, latency, reliability, user experienced data rates, area traffic capacity, integrity, utilization, retainability, mobility, energy efficiency, or quality of service; and determine the resource allocation according to information included in the policy.

56 . The edge compute node of claim 54 , wherein the processor circuitry is configured to operate one or more machine learning models to:
determine the resource allocation; and at least one of: correlate individual data items of the telemetry data with one or more other data items of the telemetry data; correlate individual data items of the measurement data with one or more other data items of the measurement data; correlate individual data items of the measurement data with the individual data items of the telemetry data; correlate service management data with the telemetry data or the measurement data; correlate data items of the service management data related to the received measurement data with resource allocations previously generated for the edge app; correlate one or more data items of the service management data with one or more resource requirements of the of edge app; correlate the one or more data items of the service management data with one or more resource requirements of a corresponding network slice in which the edge app is to operate; correlate platform resource slices of the edge compute node with one or more network slices; predict or inferring data to compensate missing data service management data; or predict a reliability of individual components of the edge compute node based at least on the telemetry data.

57 . The edge compute node of claim 56 , wherein the resource allocation indicates to move the corresponding edge app from being operated by a first processing element of the edge compute node to be operated by a second processing element of the edge compute node, and to determine the resource allocation, the processor circuitry is configured to:
determine adjustments to hardware, software, or network resources allocated to the edge app according to a run-time priority level assigned to the edge app.

58 . The edge compute node of claim 54 , wherein the resource allocation indicates to:
dynamically increase or decrease power levels or frequency levels of a processing element operating the corresponding edge app; dynamically adjust last level cache (LLC), memory bandwidth, or interface bandwidth allocated to the corresponding edge app; scale up one or more of hardware, software, or resources for the corresponding edge app; or scale down one or more of hardware, software, or resources for the corresponding edge app.

59 . The edge compute node of claim 54 , wherein the processor circuitry is configured to:
send, via the NIC, the resource allocation to a service management and orchestration framework for management of resources of multiple edge compute nodes.

60 . The edge compute node of claim 54 , wherein, to configure the at least one NAN, the processor circuitry is configured to:
configure a real-time (RT) control loop operated by the at least one NAN; and configure a near-RT control loop operated by the edge compute node, wherein the near-RT control loop operates according to a first time scale, the RT control loop operates according to a second time scale, and the first time scale is larger than the second time scale.

61 . The edge compute node of claim 60 , wherein individual sets of the telemetry data are classified as belonging to a corresponding tier of a set of data tiers, individual sets of the measurement data are classified as belonging to a corresponding tier of a set of data tiers, and each tier of the set of data tiers corresponds to a timescale of a control loop of a set of control loops, wherein the set of control loops includes the RT control loop and the near-RT control loop.

62 . The edge compute node of claim 61 , wherein a first tier of the set of data tiers includes RT reference and response data, a second tier of the set of data tiers includes data that require RT calculation or processing, a third tier of the set of data tiers includes data that require near-RT calculation or processing, and a fourth tier of the set of data tiers includes data that is used for non-RT calculation or processing.

63 . The edge compute node of claim 62 , wherein the set of edge apps include one or more of one or more artificial intelligence or machine learning apps, one or more radio resource management functions, one or more self-organizing network functions, one or more network function automation apps, and one or more policy apps, one or more interference management functions, one or more radio connection management functions, one or more flow management functions, and one or more mobility management functions.

64 . The edge compute node of claim 54 , wherein the set of NANs includes a set of radio access network functions (RANFs) of a next generation (NG) RAN architecture, the edge compute node operates a RAN intelligent controller (RIC) of an O-RAN Alliance (O-RAN) framework, and the set of edge apps include one or more non-RT RIC apps (xApps) or one or more non-RT RIC applications (rApps).

65 . A non-transitory computer readable medium (NTCRM) comprising instructions for operating an application (app), wherein execution of the instructions by one or more processors is to cause an edge compute node to:
receive measurement data from a set of network access nodes (NANs) connected to the edge compute node; receive telemetry data from one or more telemetry agents implemented by the edge compute node; determine a resource allocation for a corresponding edge app of a set of edge apps based on the measurement data and the telemetry data; and configure at least one NAN of the set of NANs or the edge compute node according to the determined resource allocation such that resources indicated by the resource allocation are allocated to the corresponding edge app.

66 . The NTCRM of claim 65 , wherein execution of the instructions is to cause the edge compute node to:
receive a policy from a orchestration function, wherein the information included in the policy includes a set of key performance measurements (KPMs), key performance indicators (KPIs), service level agreement (

Assignees

• Intel Corp

Inventors

• Ranganath Sunku
• Moustafa Hassnaa
• Nikopour Hosein
• Browne John
• Palermo Stephen T
• Parker Valerie J

Classifications

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/045

  Combinations of networks · CPC title