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    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    251.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 审中-公开

    公开(公告)号：US20200304530A1

    公开(公告)日：2020-09-24

    申请号：US16894332

    申请日：2020-06-05

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L12/24

    Abstract: In one embodiment, a device obtains characteristics of a first anomaly detection model executed by a first distributed learning agent in a network. The device receives a query from a second distributed learning agent in the network that requests identification of a similar anomaly detection to that of a second anomaly detection model executed by the second distributed learning agent. The device identifies, after receiving the query from the second distributed learning agent, the first anomaly detection model as being similar to that of the second anomaly detection model, based on the characteristics of the first anomaly detection model. The device causes the first anomaly detection model to be sent to the second distributed learning agent for execution.

    Using machine learning based on cross-signal correlation for root cause analysis in a network assurance service
    253.
    发明授权
    Using machine learning based on cross-signal correlation for root cause analysis in a network assurance service 有权

    公开(公告)号：US10785090B2

    公开(公告)日：2020-09-22

    申请号：US15983437

    申请日：2018-05-18

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Santosh Ghanshyam Pandey ,  Vikram Kumaran

    IPC: H04L12/24 ,  H04L12/26 ,  G06N20/00 ,  H04L29/12

    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.

    Using random forests to generate rules for causation analysis of network anomalies
    254.
    发明授权
    Using random forests to generate rules for causation analysis of network anomalies 有权

    公开(公告)号：US10771313B2

    公开(公告)日：2020-09-08

    申请号：US15881909

    申请日：2018-01-29

    Applicant: Cisco Technology, Inc.

    Inventor： David Tedaldi ,  Grégory Mermoud ,  Jean-Philippe Vasseur

    IPC: H04L12/24

    Abstract: In one embodiment, a network assurance service receives one or more sets of network characteristics of a network, each network characteristic forming a different feature dimension in a multi-dimensional feature space. The network assurance service applies machine learning-based anomaly detection to the one or more sets of network characteristics, to label each set of network characteristics as anomalous or non-anomalous. The network assurance service identifies, based on the labeled one or more sets of network characteristics, an anomaly pattern as a collection of unidimensional cutoffs in the feature space. The network assurance service initiates a change to the network based on the identified anomaly pattern.

    DETECTING SEASONAL CONGESTION IN SDN NETWORK FABRICS USING MACHINE LEARNING
    256.
    发明申请
    DETECTING SEASONAL CONGESTION IN SDN NETWORK FABRICS USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20200252300A1

    公开(公告)日：2020-08-06

    申请号：US16268796

    申请日：2019-02-06

    Applicant: Cisco Technology, Inc.

    Inventor： Eric M. Levy-Abegnoli ,  Jean-Philippe Vasseur ,  Pascal Thubert ,  Patrick Wetterwald

    IPC: H04L12/24 ,  H04L12/26 ,  H04L12/803

    Abstract: In one embodiment, a supervisory device for a software defined networking (SDN) fabric obtains telemetry data regarding congestion levels on a plurality of links in the SDN fabric. The supervisory device predicts seasonal congestion on a particular one of the plurality of links by using the telemetry data as input to a machine learning-based model. The supervisory device identifies a period of time associated with the predicted seasonal congestion on the particular link. The supervisory device initiates, in advance of the identified period of time, re-computation of equal-cost multi-path (ECMP) weights associated with the plurality of links that prevent occurrence of the predicted seasonal congestion on the particular link during the identified period of time.

    Technique for selecting a path computation element based on response time delay
    257.
    发明授权
    Technique for selecting a path computation element based on response time delay 有权

    公开(公告)号：US10721156B2

    公开(公告)日：2020-07-21

    申请号：US15660802

    申请日：2017-07-26

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  David R. Oran

    IPC: H04L12/721 ,  H04L12/801 ,  H04L29/08 ,  H04L12/46 ,  H04L29/06

    Abstract: A technique efficiently selects a path computation element (PCE) to compute a path between nodes of a computer network. The PCE selection technique is illustratively based on dynamic advertisements of the PCE's available path computation resources, namely a predictive response time (PRT). To that end, the novel technique enables one or more PCEs to dynamically send (advertise) their available path computation resources to one or more path computation clients (PCCs). In addition, the technique enables the PCC to efficiently select a PCE (or set of PCEs) to service a path computation request based upon those available resources.

    Hierarchical models using self organizing learning topologies
    258.
    发明授权
    Hierarchical models using self organizing learning topologies 有权

    公开(公告)号：US10701095B2

    公开(公告)日：2020-06-30

    申请号：US16190756

    申请日：2018-11-14

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L12/24

    Abstract: In one embodiment, a device in a network maintains a plurality of anomaly detection models for different sets of aggregated traffic data regarding traffic in the network. The device determines a measure of confidence in a particular one of the anomaly detection models that evaluates a particular set of aggregated traffic data. The device dynamically replaces the particular anomaly detection model with a second anomaly detection model configured to evaluate the particular set of aggregated traffic data and has a different model capacity than that of the particular anomaly detection model. The device provides an anomaly event notification to a supervisory controller based on a combined output of the second anomaly detection model and of one or more of the anomaly detection models in the plurality of anomaly detection models.

    Network configuration change analysis using machine learning
    259.
    发明授权
    Network configuration change analysis using machine learning 有权

    公开(公告)号：US10680889B2

    公开(公告)日：2020-06-09

    申请号：US15942665

    申请日：2018-04-02

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Vinay Kumar Kolar ,  Santosh Ghanshyam Pandey

    IPC: H04L12/24 ,  H04L12/26 ,  G06N5/04 ,  G06N20/00

    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.

    Dynamic selection of models for hybrid network assurance architectures
    260.
    发明授权
    Dynamic selection of models for hybrid network assurance architectures 审中-公开

    公开(公告)号：US10673728B2

    公开(公告)日：2020-06-02

    申请号：US15880689

    申请日：2018-01-26

    Applicant: Cisco Technology, Inc.

    Inventor： Andrea Di Pietro ,  Jean-Philippe Vasseur ,  Javier Cruz Mota ,  Grégory Mermoud

    IPC: H04L12/26 ,  H04L12/24

    Abstract: In one embodiment, a local service of a network reports configuration information regarding the network to a cloud-based network assurance service. The local service receives a classifier selected by the cloud-based network assurance service based on the configuration information regarding the network. The local service classifies, using the received classifier, telemetry data collected from the network, to select a modeling strategy for the network. The local service installs, based on the modeling strategy for the network, a machine learning-based model to the local service for monitoring the network.

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