H04L43/091

SYSTEMS AND METHODS FOR DETERMINING PROBLEMATIC PATHS BETWEEN INTEREST POINTS IN A MULTI-CLOUD ENVIRONMENT

In one embodiment, a method includes identifying a problematic event between a first interest point and a second interest point of a network and activating, in response to identifying the problematic event between the first interest point and the second interest point, a first endpoint associated with the first interest point and a second endpoint associated with the second interest point. The method also includes receiving, from the first endpoint and the second endpoint, telemetry data associated with a problematic path between the first interest point and the second interest point. The method further includes determining the problematic path between the first interest point and the second interest point using the telemetry data received from the first endpoint and the second endpoint.

VIRTUAL FUNCTION PERFORMANCE ANALYSIS SYSTEM AND ANALYSIS METHOD THEREOF

A virtual function performance analysis system and an analysis method thereof are disclosed. The virtual function performance analysis method includes: monitoring performance of at least one virtual function of a virtual network function application on a virtual platform having at least one physical resource and at least one virtual resource; monitoring and recording an actual value of each of performance indicators of each of physical resources, each of virtual resources and each of virtual functions; comparing the actual value of each of performance indicators with the associated expected value and/or threshold value to obtain a comparison result; and analyzing system performance according to the comparison result.

VIRTUAL FUNCTION PERFORMANCE ANALYSIS SYSTEM AND ANALYSIS METHOD THEREOF

A virtual function performance analysis system and an analysis method thereof are disclosed. The virtual function performance analysis method includes: monitoring performance of at least one virtual function of a virtual network function application on a virtual platform having at least one physical resource and at least one virtual resource; monitoring and recording an actual value of each of performance indicators of each of physical resources, each of virtual resources and each of virtual functions; comparing the actual value of each of performance indicators with the associated expected value and/or threshold value to obtain a comparison result; and analyzing system performance according to the comparison result.

Data management in an edge network

Generally discussed herein are systems, devices, and methods for data management in a reverse content data network (rCDN). A component of the rCDN may include a memory to hold content received from a first sensor device of a plurality of sensor devices of the rCDN and first attributes that describe properties of the content. The component may include processing circuitry to receive second content from a second sensor device of the plurality of sensor devices, the second content including a plurality of second attributes that describe properties of the second content, and forward, in response to a determination, based on the first and second attributes, that there is insufficient space to store the second content on the memory, the second content to a node of the rCDN that is fewer hops away from a backend cloud than the component.

Data management in an edge network

Generally discussed herein are systems, devices, and methods for data management in a reverse content data network (rCDN). A component of the rCDN may include a memory to hold content received from a first sensor device of a plurality of sensor devices of the rCDN and first attributes that describe properties of the content. The component may include processing circuitry to receive second content from a second sensor device of the plurality of sensor devices, the second content including a plurality of second attributes that describe properties of the second content, and forward, in response to a determination, based on the first and second attributes, that there is insufficient space to store the second content on the memory, the second content to a node of the rCDN that is fewer hops away from a backend cloud than the component.

Determining a service impact score for a metric according to a scope of the metric
11477095 · 2022-10-18 · ·

A scoring platform may obtain a set of measurements associated with a service metric, wherein the service metric is associated with a service of a network. The scoring platform may determine, based on the set of measurements, an aggregation score associated with the service metric. The scoring platform may determine a scope score associated with the set of measurements, wherein the scope score is based on a quantity of units associated with the set of measurements. The scoring platform may determine a service impact score associated with the service metric based on the aggregation score and the scope score, wherein the service impact score is representative of a contributive effect associated with the service metric. The scoring platform may perform an action associated with the service impact score to permit a source of interest associated with the service metric to be detected.

PROVISION OF DATA ANALYTICS IN A TELECOMMUNICATION NETWORK
20230164686 · 2023-05-25 ·

A communication method and a system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT) is provided. The disclosure is applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as a smart home, a smart building, a smart city, a smart car, a connected car, health care, digital education, a smart retail, security and safety services. A method performed by a first entity performing a network data analytics function (NWDAF) is provided. The method includes receiving, from a second entity performing network function (NF), a first message for requesting observed service experience analytics, the first message including single-network slice selection assistance information (S-NSSAI) indicating a network slice, transmitting, to a third entity performing application function (AF) associated with the S-NSSAI, a second message for requesting service data associated with the observed service experience analytics, the second message including information on at least one application, receiving, from the third entity, the service data including at least one service experience for the at least one application, identifying the observed service experience analytics based on the service data, and transmitting, to the second entity, the observed service experience analytics.

Method and apparatus for providing trouble isolation via a network

A method and apparatus for providing trouble isolation are disclosed. For example, the method monitors a plurality of sessions for a user group for detecting an abnormal cause code associated with the user group, determines a root cause for the abnormal cause code when a deviation is determined to have occurred for the cause code of the user group, wherein the root cause identifies either an issue associated with the communications network or an issue associated with user endpoint devices of the user group, and generates a ticket indicating the root cause.

Method and apparatus for providing trouble isolation via a network

A method and apparatus for providing trouble isolation are disclosed. For example, the method monitors a plurality of sessions for a user group for detecting an abnormal cause code associated with the user group, determines a root cause for the abnormal cause code when a deviation is determined to have occurred for the cause code of the user group, wherein the root cause identifies either an issue associated with the communications network or an issue associated with user endpoint devices of the user group, and generates a ticket indicating the root cause.

Intelligent lifecycle management of analytic functions for an IoT intelligent edge with a hypergraph-based approach

The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.