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公开(公告)号：US20220294730A1
公开(公告)日：2022-09-15
申请号：US17196429
申请日：2021-03-09
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Vinay Kumar Kolar , Jürg Nicolaus Diemand
IPC: H04L12/723 , H04L12/721 , H04L12/725 , H04L12/24 , H04L12/46
Abstract: In one embodiment, a controller for a network receives, via a user interface, a downgrade policy for the network that specifies an objective for path downgrades in the network. The controller identifies traffic of an application conveyed by an edge router in the network via a particular path in the network and using a first type of link. The controller predicts an effect of downgrading the particular path from using the first type of link to using a second type of link to convey the traffic of the application. The controller causes the edge router to convey the traffic of the application via the second type of link, when the effect predicted by the controller satisfies the objective specified by the downgrade policy.
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公开(公告)号：US11438406B2
公开(公告)日：2022-09-06
申请号：US16865517
申请日：2020-05-04
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Pierre-André Savalle , Vinay Kumar Kolar , David Tedaldi
Abstract: In one embodiment, a device deploys a first machine learning model to an inference location in a network. The first machine learning model is used at the inference location to make inferences about the network. The device receives, from the inference location, an indication that the first machine learning model is exhibiting poor performance. The device identifies a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device. The device deploys, based on the corrective measure, a second machine learning model to the inference location. The second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.
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63.发明授权公开(公告)号：US11258673B2
公开(公告)日：2022-02-22
申请号：US16402384
申请日：2019-05-03
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Vinay Kumar Kolar
Abstract: In one embodiment, a supervisory service for one or more networks receives telemetry data samples from a plurality of networking devices in the one or more networks. The service trains a failure prediction model to predict failures in the one or more networks, using a training dataset comprising the received telemetry data samples. The service assesses performance of the failure prediction model. The service trains, based on the assessed performance of the failure prediction model, a machine learning-based classification model to determine whether a networking device should send a particular telemetry data sample to the service. The service sends the machine learning-based classifier to one or more of the plurality of networking devices, to control which telemetry data samples the one or more networking devices send to the supervisory service.
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公开(公告)号：US20210360059A1
公开(公告)日：2021-11-18
申请号：US16875182
申请日：2020-05-15
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Jean-Philippe Vasseur , Grégory Mermoud , Vinay Kumar Kolar
Abstract: In one embodiment, a network assurance service that monitors one or more networks identifies changes in a key performance indicator for each of a plurality of network entities in the one or more networks. The service forms a peer group of network entities from the plurality of network entities whose changes in the key performance indicator are correlated. The service monitors the key performance indicator for network entities in the peer group of network entities. The service, based on the monitoring, detects an anomalous change in the key performance indicator for a particular network entity in the peer group of network entities relative to other network entities in the peer group of network entities.
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公开(公告)号：US20210344745A1
公开(公告)日：2021-11-04
申请号：US16865517
申请日：2020-05-04
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Pierre-André Savalle , Vinay Kumar Kolar , David Tedaldi
Abstract: In one embodiment, a device deploys a first machine learning model to an inference location in a network. The first machine learning model is used at the inference location to make inferences about the network. The device receives, from the inference location, an indication that the first machine learning model is exhibiting poor performance. The device identifies a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device. The device deploys, based on the corrective measure, a second machine learning model to the inference location. The second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.
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Patent Agency Ranking
公开(公告)号：US20210336871A1
公开(公告)日：2021-10-28
申请号：US16856399
申请日：2020-04-23
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Vinay Kumar Kolar
IPC: H04L12/707 , H04L12/725 , H04L12/911 , H04L12/927
Abstract: In one embodiment, a device in a network obtains tunnel flappiness metrics associated with a particular tunnel in the network exhibiting flapping. The device makes, based on the tunnel flappiness metrics, a prediction that the particular tunnel is going to flap. The prediction is made using a machine learning model. The device proactively reroutes, based on the prediction, traffic from the particular tunnel onto an alternate tunnel, prior to the particular tunnel flapping. The device evaluates performance of the alternate tunnel, after proactively rerouting the traffic from the particular tunnel onto the alternate tunnel.
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公开(公告)号：US20210281504A1
公开(公告)日：2021-09-09
申请号：US17330720
申请日：2021-05-26
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Vinay Kumar Kolar , Grégory Mermoud
Abstract: In one embodiment, a device obtains performance data regarding failures of a tunnel in a network. The device generates a failure profile for the tunnel by applying machine learning to the performance data regarding the failures of the tunnel. The device determines, based on the failure profile for the tunnel, whether the tunnel exhibits failure flapping behavior. The device adjusts one or more Bidirectional Forwarding Detection (BFD) probing timers used to detect failures of the tunnel, based on the determination as to whether the tunnel exhibits failure flapping behavior.
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公开(公告)号：US20210218641A1
公开(公告)日：2021-07-15
申请号：US16740051
申请日：2020-01-10
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Vinay Kumar Kolar , Pierre-Andre Savalle
IPC: H04L12/24 , H04L12/46 , H04L12/703 , G06N20/00
Abstract: In one embodiment, a service receives input data from networking entities in a network. The input data comprises synchronous time series data, asynchronous event data, and an entity graph that that indicates relationships between the networking entities in the network. The service clusters the networking entities by type in a plurality of networking entity clusters. The service selects, based on a combination of the received input data, machine learning model data features. The service trains, using the selected machine learning model data features, a machine learning model to forecast a key performance indicator (KPI) for a particular one of the networking entity clusters.
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公开(公告)号：US11049033B2
公开(公告)日：2021-06-29
申请号：US15869639
申请日：2018-01-12
Applicant: Cisco Technology, Inc.
Inventor： Vinay Kumar Kolar , Vikram Kumaran , Abhishek Kumar , Santosh Ghanshyam Pandey , Jean-Philippe Vasseur , Grégory Mermoud
Abstract: In one embodiment, a network assurance system that monitors a network labels time periods with positive labels, based on the network assurance system detecting problems in the network during the time periods. The network assurance system assigns tags to discrete portions of a feature space of measurements from the monitored network, based on whether a particular range of values in the feature space has a threshold probability of occurring during a positively-labeled time period. The network assurance system determines a set of the assigned tags that frequently co-occur with the positively-labeled time periods in which problems are detected in the network. The network assurance system causes performance of a mitigation action in the network based on the set of assigned tags that frequently co-occur with the positively-labeled time periods.
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公开(公告)号：US20210158106A1
公开(公告)日：2021-05-27
申请号：US16692165
申请日：2019-11-22
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Vinay Kumar Kolar , Andrea Di Pietro , Grégory Mermoud , Pierre-Andre Savalle
Abstract: In one embodiment, a service computes a data fidelity metric for network telemetry data used by a machine learning model to monitor a computer network. The service detects unacceptable performance of the machine learning model. The service determines a correlation between the data fidelity metric and the unacceptable performance of the machine learning model. The service adjusts generation of the network telemetry data for input to the machine learning model, based on the determined correlation between the data fidelity metric and the unacceptable performance of the machine learning model.
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