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    AVOIDING USER EXPERIENCE DISRUPTIONS USING CONTEXTUAL MULTI-ARMED BANDITS
    11.
    发明申请
    AVOIDING USER EXPERIENCE DISRUPTIONS USING CONTEXTUAL MULTI-ARMED BANDITS 有权

    公开(公告)号：US20230035691A1

    公开(公告)日：2023-02-02

    申请号：US17389823

    申请日：2021-07-30

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Vinay Kumar Kolar

    IPC: H04L12/707 ,  H04L12/751

    Abstract: In one embodiment, a device uses a multi-armed bandit model to select different network paths over time via which traffic associated with an online application is routed. The device obtains, from a provider of the online application, application experience metrics associated with the different network paths and indicative of user satisfaction with the online application. The device learns, by the multi-armed bandit model, which of the different network paths will provide satisfactory application experience metrics, based on the application experience metrics associated with the different network paths. The device causes the traffic associated with the online application to be routed via a set of one or more paths expected by the multi-armed bandit model to provide satisfactory application experience metrics for the online application.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    12.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 有权

    公开(公告)号：US20220353285A1

    公开(公告)日：2022-11-03

    申请号：US17677541

    申请日：2022-02-22

    Applicant: Cisco Technology, Inc.

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

    IPC: H04L9/40 ,  H04L41/142

    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.

    ANOMALY DETECTION OF MODEL PERFORMANCE IN AN MLOPS PLATFORM
    13.
    发明申请
    ANOMALY DETECTION OF MODEL PERFORMANCE IN AN MLOPS PLATFORM 有权

    公开(公告)号：US20220353166A1

    公开(公告)日：2022-11-03

    申请号：US17696532

    申请日：2022-03-16

    Applicant: Cisco Technology, Inc.

    Inventor： Vinay Kumar Kolar ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Pierre-André Savalle

    IPC: H04L43/00 ,  G06F17/18 ,  G06K9/62 ,  G06N20/00

    Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

    GLOBALLY AVOIDING SIMULTANEOUS REROUTES IN A NETWORK
    15.
    发明申请
    GLOBALLY AVOIDING SIMULTANEOUS REROUTES IN A NETWORK 有权

    公开(公告)号：US20220294738A1

    公开(公告)日：2022-09-15

    申请号：US17829435

    申请日：2022-06-01

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Vinay Kumar Kolar

    IPC: H04L47/127 ,  H04L47/2475 ,  H04L47/28

    Abstract: In one embodiment, a device obtains, from a plurality of routers in a network, a set of routing policies that collectively specify a first set of paths in the network, a second set of paths in the network, and time periods during which traffic is to be rerouted from one of the first set of paths to one of the second set of paths in the network. The device identifies overlapping path segments of the second set of paths in the network. The device makes, based in part on the overlapping path segments, a prediction that two or more of the set of routing policies will cause congestion along paths with overlapping path segments. The device adjusts, based on the prediction, the set of routing policies, to avoid causing the congestion.

    Anomaly detection of model performance in an MLOps platform
    17.
    发明授权
    Anomaly detection of model performance in an MLOps platform 有权

    公开(公告)号：US11310141B2

    公开(公告)日：2022-04-19

    申请号：US16710836

    申请日：2019-12-11

    Applicant: Cisco Technology, Inc.

    Inventor： Vinay Kumar Kolar ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Pierre-Andre Savalle

    IPC: H04L12/26 ,  G06N20/00 ,  H04L43/00 ,  G06F17/18 ,  G06K9/62

    Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

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