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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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102.发明申请公开(公告)号：US20210303598A1
公开(公告)日：2021-09-30
申请号：US16830717
申请日：2020-03-26
Applicant: Cisco Technology, Inc,
Inventor： Grégory Mermoud , David Tedaldi , Pierre-André Savalle , Jean-Philippe Vasseur , Jürg Nicolaus Diemand
Abstract: In various embodiments, a device classification service obtains data indicative of device attributes of a plurality of devices. The device classification service forms, based on the obtained data indicative of the device attributes, a concept graph that comprises nodes that represent different sets of the device attributes. The device classification service determines, by analyzing the concept graph, a relevance score for each of the device attributes that quantifies how relevant that attribute is to classifying a device by its device type. The device classification service uses the relevance scores for the device attributes to cluster the plurality of devices into device type clusters by their device attributes.
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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：US11063836B2
公开(公告)日：2021-07-13
申请号：US15464526
申请日：2017-03-21
Applicant: Cisco Technology, inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur
Abstract: In one embodiment, a device in a network receives data regarding a plurality of predefined health status rules that evaluate one or more observed conditions of the network. The device, using the data regarding the plurality of health status rules for the network, trains a machine learning-based classifier to generate predictions regarding outputs of the health status rules. The device adjusts the machine learning-based classifier based on feedback associated with the generated predictions. The device provides an indication of one or more of the predictions regarding the outputs of the health status rules to a user interface.
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Patent Agency Ranking
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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公开(公告)号：US20210126833A1
公开(公告)日：2021-04-29
申请号：US17142447
申请日：2021-01-06
Applicant: Cisco Technology, Inc.
Inventor： David Tedaldi , Grégory Mermoud , Pierre-Andre Savalle , Jean-Philippe Vasseur
Abstract: In various embodiments, a device classification service obtains traffic telemetry data for a plurality of devices in a network. The service applies clustering to the traffic telemetry data, to form device clusters. The service generates a device classification rule based on a particular one of the device clusters. The service receives feedback from a user interface regarding the device classification rule. The service adjusts the device classification rule based on the received feedback.
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109.发明申请公开(公告)号：US20200351173A1
公开(公告)日：2020-11-05
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：US20200314022A1
公开(公告)日：2020-10-01
申请号：US16362819
申请日：2019-03-25
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Vinay Kumar Kolar
IPC: H04L12/911 , H04L12/46 , H04L12/707 , H04L12/703 , G06K9/62 , G06N20/00
Abstract: In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) obtains telemetry data from one or more edge devices in the SD-WAN. The service trains, using the telemetry data as training data, a machine learning- based model to predict tunnel failures in the SD-WAN. The service receives feedback from the one or more edge devices regarding failure predictions made by the trained machine learning-based model. The service retrains the machine learning-based model, based on the received feedback.
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