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.

    Using machine learning based on cross-signal correlation for root cause analysis in a network assurance service

    公开(公告)号:US10785090B2

    公开(公告)日:2020-09-22

    申请号:US15983437

    申请日:2018-05-18

    Abstract: In one embodiment, a network assurance service associates a target key performance indicator (tKPI) measured from a network with a plurality of causation key performance indicators (cKPIs) measured from the network that may indicate a root cause of a tKPI anomaly. The network assurance service applies a machine learning-based anomaly detector to the tKPI over time, to generate tKPI anomaly scores. The network assurance service calculates, for each of cKPIs, a mean and standard deviation of that cKPI using a plurality of different time windows associated with the tKPI anomaly scores. The network assurance service uses the calculated means and standard deviations of the cKPIs in the different time windows to calculate cross-correlation scores between the tKPI anomaly scores and the cKPIs. The network assurance service selects one or more of the cKPIs as the root cause of the tKPI anomaly based on their calculated cross-correlation scores.

    Network configuration change analysis using machine learning

    公开(公告)号:US10680889B2

    公开(公告)日:2020-06-09

    申请号:US15942665

    申请日:2018-04-02

    Abstract: In one embodiment, a network assurance service that monitors one or more networks receives data indicative of networking device configuration changes in the one or more networks. The service also receives one or more performance indicators for the one or more networks. The service trains a machine learning model based on the received data indicative of the networking device configuration changes and on the received one or more performance indicators for the one or more networks. The service predicts, using the machine learning model, a change in the one or more performance indicators that would result from a particular networking device configuration change. The service causes the particular networking device configuration change to be made in the network based on the predicted one or more performance indicators.

    LOCATION ACCURACY ASSESSMENT AND REMEDIATION FOR INDOOR POSITIONING SYSTEM DEPLOYMENTS

    公开(公告)号:US20200015189A1

    公开(公告)日:2020-01-09

    申请号:US16029000

    申请日:2018-07-06

    Abstract: In one embodiment, a device determines that location accuracy performance of an indoor positioning system deployment is below a predefined threshold. The device obtains characteristic data for the indoor positioning system deployment. The device identifies, by using the characteristic data as input to a machine learning model, one or more contributing factors from the characteristic data for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold. The device initiates a remediation action based on the identified one or more contributing factors for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold.

    USING MACHINE LEARNING BASED ON CROSS-SIGNAL CORRELATION FOR ROOT CAUSE ANALYSIS IN A NETWORK ASSURANCE SERVICE

    公开(公告)号:US20190356533A1

    公开(公告)日:2019-11-21

    申请号:US15983437

    申请日:2018-05-18

    Abstract: In one embodiment, a network assurance service associates a target key performance indicator (tKPI) measured from a network with a plurality of causation key performance indicators (cKPIs) measured from the network that may indicate a root cause of a tKPI anomaly. The network assurance service applies a machine learning-based anomaly detector to the tKPI over time, to generate tKPI anomaly scores. The network assurance service calculates, for each of cKPIs, a mean and standard deviation of that cKPI using a plurality of different time windows associated with the tKPI anomaly scores. The network assurance service uses the calculated means and standard deviations of the cKPIs in the different time windows to calculate cross-correlation scores between the tKPI anomaly scores and the cKPIs. The network assurance service selects one or more of the cKPIs as the root cause of the tKPI anomaly based on their calculated cross-correlation scores.

    Characterizing movement behaviors of wireless nodes in a network

    公开(公告)号:US10425912B1

    公开(公告)日:2019-09-24

    申请号:US16250043

    申请日:2019-01-17

    Abstract: In one embodiment, a device receives location estimates for a wireless node in a network, each location estimate having an associated timestamp. The device applies hierarchical clustering to the received location estimates and their associated timestamps, to identify locations and points in time in which the wireless node was stationary. The device performs sequence modeling on the identified locations and points in time in which the wireless node was stationary, to form a sequence of locations and associated time periods in which the wireless node was stationary. The device associates the wireless node with a behavioral profile based on the sequence of locations and associated time periods in which the wireless node. The device generates, based in part on the behavioral profile for the wireless node, a predictive model that predicts a location of the wireless node at a particular point in time.

    Detecting incorrectly placed access points
    30.
    发明授权
    Detecting incorrectly placed access points 有权
    检测不正确的接入点

    公开(公告)号:US09310463B2

    公开(公告)日:2016-04-12

    申请号:US14072851

    申请日:2013-11-06

    CPC classification number: G01S5/0252 G01S5/021 G01S5/0242 H04W64/003

    Abstract: Embodiments provide techniques for detecting access points on a position map, particularly incorrectly placed access points. For each access point in a plurality of access points, a subset of the plurality of access points that neighbor the access point are identified. Embodiments estimate a location of the access point, based on a respective indication of signal strength from each neighboring access point in the subset of access points and a respective position of each of the neighboring access points in position map. A difference between a recorded position of the access point in the position map and the estimated location of the access point is calculated. Embodiments then determine that the position within the position map for a first one of the plurality of access points is incorrect, based on the determined difference for the first access point.

    Abstract translation: 实施例提供了用于检测位置图上的接入点的技术,特别是不正确放置的接入点。 对于多个接入点中的每个接入点,识别与接入点相邻的多个接入点的子集。 实施例基于来自接入点子集中的每个相邻接入点的信号强度的相应指示和位置图中的每个相邻接入点的相应位置估计接入点的位置。 计算位置图中的接入点的记录位置与接入点的估计位置之间的差异。 然后,实施例基于所确定的第一接入点的差异,确定位置图中位于多个接入点中的第一接入点的位置是不正确的。

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