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公开(公告)号：US20200379839A1
公开(公告)日：2020-12-03
申请号：US16429379
申请日：2019-06-03
Applicant: Cisco Technology, Inc.
Inventor： Pierre-Andre Savalle , Jean-Philippe Vasseur , Grégory Mermoud
Abstract: In one embodiment, a device predicts a failure of a first tunnel in a software-defined wide area network (SD-WAN). The device determines that no backup tunnel for the first tunnel exists in the SD-WAN that can satisfy one or more service level agreements (SLAs) of traffic on the first tunnel, were the traffic rerouted from the first tunnel onto that tunnel. The device predicts, using a machine learning model, that a backup tunnel for the first tunnel exists in the SD-WAN that can satisfy an SLA of a subset of the traffic on the first tunnel, in response to determining that no backup tunnel exists in the SD-WAN that can satisfy the one or more SLAs of the traffic on the first tunnel. The device proactively reroutes the subset of the traffic on the first tunnel onto the backup tunnel, in advance of the predicted failure of the first tunnel.
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22.发明授权公开(公告)号：US11409516B2
公开(公告)日：2022-08-09
申请号：US16709307
申请日：2019-12-10
Applicant: Cisco Technology, Inc.
Inventor： Vinay Kumar Kolar , Jean-Philippe Vasseur , Grégory Mermoud , Pierre-Andre Savalle
Abstract: In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.
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公开(公告)号：US11290331B2
公开(公告)日：2022-03-29
申请号：US16428202
申请日：2019-05-31
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Pierre-Andre Savalle , David Tedaldi
IPC: H04L12/24 , H04L29/06 , H04L12/723 , H04L41/0873 , H04L45/50 , H04L41/0893 , H04L41/0816
Abstract: In one embodiment, a service receives a plurality of device type classification rules, each rule comprising a device type label and one or more device attributes used as criteria for application of the label to a device in a network. The service estimates, across a space of the device attributes, device densities of devices having device attributes at different points in that space. The service uses the estimated device densities to identify two or more of the device type classification rules as having overlapping device attributes. The service determines that the two or more device type classification rules are in conflict, based on the two or more rules having different device type labels. The service generates a rule conflict resolution that comprises one of the device type labels from the conflicting two or more device type classification rules.
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公开(公告)号：US11196629B2
公开(公告)日：2021-12-07
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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Patent Agency Ranking
公开(公告)号：US11153347B2
公开(公告)日：2021-10-19
申请号：US16424912
申请日：2019-05-29
Applicant: Cisco Technology, Inc.
Inventor： Pierre-Andre Savalle , Jean-Philippe Vasseur , Grégory Mermoud
Abstract: In one embodiment, a device in a network obtains data indicative of a device classification rule, a device type label associated with the rule, and a set of positive and negative feature vectors used to create the rule. The device replaces similar feature vectors in the set of positive and negative feature vectors with a single feature vector, to form a reduced set of feature vectors. The device applies differential privacy to the reduced set of feature vectors. The device sends a digest to a cloud service. The digest comprises the device classification rule, the device type label, and the reduced set of feature vectors to which differential privacy was applied. The service uses the digest to train a machine learning-based device classifier.
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公开(公告)号：US11063842B1
公开(公告)日：2021-07-13
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 , G06N20/00 , H04L12/703 , H04L12/46
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：US20210184958A1
公开(公告)日：2021-06-17
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
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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28.发明申请公开(公告)号：US20210173636A1
公开(公告)日：2021-06-10
申请号：US16709307
申请日：2019-12-10
Applicant: Cisco Technology, Inc.
Inventor： Vinay Kumar Kolar , Jean-Philippe Vasseur , Gregory Mermoud , Pierre-Andre Savalle
Abstract: In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.
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公开(公告)号：US20210158260A1
公开(公告)日：2021-05-27
申请号：US16693594
申请日：2019-11-25
Applicant: Cisco Technology, Inc.
Inventor： Vinay Kumar Kolar , Jean-Philippe Vasseur , Vikram Kumaran , Grégory Mermoud , Pierre-Andre Savalle
Abstract: In one embodiment, a network assurance service that monitors a network receives key performance indicators (KPIs) for a plurality of network entities in the network. The service applies clustering to the KPIs, to form KPI clusters. The service designates the network entities associated with the particular KPI cluster as belonging to a peer group, based in part on an assessment that the network entities associated with the particular KPI cluster share one or more attributes. The service uses a machine learning model to identify one of the network entities in the peer group as anomalous among the network entities in the peer group.
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