THRESHOLD SELECTION FOR KPI CANDIDACY IN ROOT CAUSE ANALYSIS OF NETWORK ISSUES

    公开(公告)号:US20220231903A1

    公开(公告)日:2022-07-21

    申请号:US17716913

    申请日:2022-04-08

    Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

    DETERMINING THE IMPORTANCE OF NETWORK DEVICES BASED ON DISCOVERED TOPOLOGY, MANAGED ENDPOINTS, AND ACTIVITY

    公开(公告)号:US20200267054A1

    公开(公告)日:2020-08-20

    申请号:US16280686

    申请日:2019-02-20

    Abstract: Determining the importance of network devices based on a discovered topology, managed endpoints, and activity may be provided. First, topology data may be received corresponding to a network comprising a plurality of devices. Then, a topology matrix may be created representing the topology data. Next, a stationary matrix may be determined from the topology matrix. The stationary matrix may indicate a relative importance of each of the plurality of devices within the network. A health score may then be determined for at least one of the plurality of devices based on the relative importance of the at least one of the plurality of devices. The health score may be determined using the stationary matrix.

    Determining the importance of network devices based on discovered topology, managed endpoints, and activity

    公开(公告)号:US10897401B2

    公开(公告)日:2021-01-19

    申请号:US16280686

    申请日:2019-02-20

    Abstract: Determining the importance of network devices based on a discovered topology, managed endpoints, and activity may be provided. First, topology data may be received corresponding to a network comprising a plurality of devices. Then, a topology matrix may be created representing the topology data. Next, a stationary matrix may be determined from the topology matrix. The stationary matrix may indicate a relative importance of each of the plurality of devices within the network. A health score may then be determined for at least one of the plurality of devices based on the relative importance of the at least one of the plurality of devices. The health score may be determined using the stationary matrix.

    Threshold selection for KPI candidacy in root cause analysis of network issues

    公开(公告)号:US11616682B2

    公开(公告)日:2023-03-28

    申请号:US17716913

    申请日:2022-04-08

    Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

    THRESHOLD SELECTION FOR KPI CANDIDACY IN ROOT CAUSE ANALYSIS OF NETWORK ISSUES

    公开(公告)号:US20210176115A1

    公开(公告)日:2021-06-10

    申请号:US17104091

    申请日:2020-11-25

    Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

    KPI trajectory-driven outlier detection in a network assurance service

    公开(公告)号:US10904114B2

    公开(公告)日:2021-01-26

    申请号:US16263323

    申请日:2019-01-31

    Abstract: In one embodiment, a network assurance service that monitors a network receives a plurality of key performance indicators (KPIs) for a networking device in the network over time. The network assurance service represents relationship changes between the KPIs over time as a set of one or more KPI trajectories. The network assurance service uses a machine learning-based model to determine that a behavior of the networking device is anomalous, based on the one or more KPI trajectories. The network assurance service provides an indication of the anomalous behavior of the networking device to a user interface.

    THRESHOLD SELECTION FOR KPI CANDIDACY IN ROOT CAUSE ANALYSIS OF NETWORK ISSUES

    公开(公告)号:US20200092159A1

    公开(公告)日:2020-03-19

    申请号:US16131143

    申请日:2018-09-14

    Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

    Threshold selection for KPI candidacy in root cause analysis of network issues

    公开(公告)号:US10897389B2

    公开(公告)日:2021-01-19

    申请号:US16131143

    申请日:2018-09-14

    Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

    KPI TRAJECTORY-DRIVEN OUTLIER DETECTION IN A NETWORK ASSURANCE SERVICE

    公开(公告)号:US20200252310A1

    公开(公告)日:2020-08-06

    申请号:US16263323

    申请日:2019-01-31

    Abstract: In one embodiment, a network assurance service that monitors a network receives a plurality of key performance indicators (KPIs) for a networking device in the network over time. The network assurance service represents relationship changes between the KPIs over time as a set of one or more KPI trajectories. The network assurance service uses a machine learning-based model to determine that a behavior of the networking device is anomalous, based on the one or more KPI trajectories. The network assurance service provides an indication of the anomalous behavior of the networking device to a user interface.

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