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261.发明申请公开(公告)号：US20200160100A1
公开(公告)日：2020-05-21
申请号：US16194442
申请日：2018-11-19
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Pierre-André Savalle , Jean-Philippe Vasseur , David Tedaldi
Abstract: In one embodiment, a device clusters traffic feature vectors for a plurality of endpoints in a network into a set of clusters. Each traffic feature vector comprises traffic telemetry data captured for one of the endpoints. The device selects one of the clusters for labeling, based in part on contextual data associated with the clusters that was not used to form the clusters. The device obtains a device type label for the selected cluster by providing data regarding the selected cluster and the contextual data associated with that cluster to a user interface. The device provides the device type label and the traffic feature vectors associated with the selected cluster for training a machine learning-based device type classifier.
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公开(公告)号：US10659333B2
公开(公告)日：2020-05-19
申请号：US15188175
申请日：2016-06-21
Applicant: Cisco Technology, Inc.
Inventor： Laurent Sartran , Pierre-André Savalle , Jean-Philippe Vasseur , Grégory Mermoud , Javier Cruz Mota , Sébastien Gay
IPC: H04L12/26
Abstract: In one embodiment, a device in a network determines cluster assignments that assign traffic data regarding traffic in the network to activity level clusters based on one or more measures of traffic activity in the traffic data. The device uses the cluster assignments to predict seasonal activity for a particular subset of the traffic in the network. The device determines an activity level for new traffic data regarding the particular subset of traffic in the network. The device detects a network anomaly by comparing the activity level for the new traffic data to the predicted seasonal activity.
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公开(公告)号：US20200153616A1
公开(公告)日：2020-05-14
申请号：US16186662
申请日：2018-11-12
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Jean-Philippe Vasseur , Alexandre Honoré , Grégory Mermoud
Abstract: In one embodiment, a network assurance service maintains a first set of telemetry data from the network anonymized using a first key regarding a plurality of network entities in a monitored network. The service receives a key rotation notification indicative of a key changeover from the first key to a second key for anonymization of a second set of telemetry data from the network. The service forms, during a key rotation time period associated with the key changeover, a mapped dataset by converting anonymized tokens in the second set of telemetry data into anonymized tokens in the first set of telemetry data. The service augments, during the key rotation time period, the first set of telemetry data with the mapped dataset. The service assesses, during the time period, performance of the network by applying a machine learning-based model to the first set of telemetry data augmented with the mapped dataset.
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公开(公告)号：US10652135B2
公开(公告)日：2020-05-12
申请号：US15216007
申请日：2016-07-21
Applicant: Cisco Technology, Inc.
Inventor： Pascal Thubert , IJsbrand Wijnands , Patrick Wetterwald , Eric Levy-Abegnoli , Jean-Philippe Vasseur
IPC: H04L12/727 , H04L12/753 , H04L12/751 , H04L12/721 , H04L29/06 , H04L12/725
Abstract: In one embodiment, a multicast listener device floods a path lookup request to search for a multicast tree, and may then receive path lookup responses from candidate nodes on the multicast tree, where each of the path lookup responses indicates a unicast routing cost from a respective candidate node to the multicast listener device, and where each of the candidate nodes is configured to suppress a path lookup response if a total path latency from a source of the multicast tree to the multicast listener device via that respective candidate node is greater than a maximum allowable path latency. The multicast listener device may then select a particular candidate node as a join point for the multicast tree based on the particular node having a lowest associated unicast routing cost to the multicast listener device from among the candidate nodes, and joins the multicast tree at the selected join point.
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公开(公告)号：US20200099590A1
公开(公告)日：2020-03-26
申请号：US16697344
申请日：2019-11-27
Applicant: Cisco Technology, Inc.
Abstract: In one embodiment, a network assurance service executing in a local network clusters measurements obtained from the local network regarding a plurality of devices in the local network into measurement clusters. The network assurance service computes aggregated metrics for each of the measurement clusters. The network assurance service sends a machine learning model computation request to a remote service outside of the local network that includes the aggregated metrics for each of the measurement clusters. The remote service uses the aggregated metrics to train a machine learning-based model to analyze the local network. The network assurance service receives the trained machine learning-based model to analyze performance of the local network. The network assurance service uses the receive machine learning-based model to analyze performance of the local network.
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Patent Agency Ranking
公开(公告)号：US10601676B2
公开(公告)日：2020-03-24
申请号：US15705462
申请日：2017-09-15
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Grégory Mermoud , Jean-Philippe Vasseur
Abstract: In one embodiment, a service identifies a performance issue exhibited by a first device in a first network. The service forms a set of one or more time series of one or more characteristics of the first device associated with the identified performance issue. The service generates a mapping between the set of one or more time series of one or more characteristics of the first device to one or more time series of one or more characteristics of a second device in a second network. The mapping comprises a relevancy score that quantifies a degree of similarity between the characteristics of the first and second devices. The service determines a likelihood of the second device exhibiting the performance issue based on the generated mapping and on the relevancy score. The service provides an indication of the determined likelihood to a user interface associated with the second network.
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公开(公告)号：US20200076677A1
公开(公告)日：2020-03-05
申请号：US16120529
申请日：2018-09-04
Applicant: Cisco Technology, Inc.
Inventor： Gregory Mermoud , David Tedaldi , Jean-Philippe Vasseur
Abstract: In one embodiment, a network assurance service that monitors a network detects a behavioral anomaly in the network using an anomaly detector that compares an anomaly detection threshold to a target value calculated based on a first set of one or more measurements from the network. The service uses an explanation model to predict when the anomaly detector will detect anomalies. The explanation model takes as input a second set of one or more measurements from the network that differs from the first set. The service determines that the detected anomaly is explainable, based on the explanation model correctly predicting the detection of the anomaly by the anomaly detector. The service provides an anomaly detection alert for the detected anomaly to a user interface, based on the detected anomaly being explainable. The anomaly detection alert indicates at least one measurement from the second set as an explanation for the anomaly.
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268.发明授权公开(公告)号：US10567267B2
公开(公告)日：2020-02-18
申请号：US15642657
申请日：2017-07-06
Applicant: Cisco Technology, Inc.
IPC: H04L12/751 , H04W24/02
Abstract: In one embodiment, a method comprises: determining access point devices providing network coverage for a mobile network device within a prescribed coverage area of a deterministic network; establishing a deterministic reception tree comprising a root and switching devices associated with the access point devices, the deterministic reception tree enabling any one or more of the switching devices to forward toward the root a data packet, transmitted by the mobile network device at a prescribed transmission time, for deterministic reception by the root at a prescribed reception time regardless of any distance of any of the access point devices from the root; and causing the switching devices to implement the deterministic reception tree enabling the root to deterministically receive the data packet, received by any one or more of the access point devices, at the prescribed reception time.
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公开(公告)号：US10540605B2
公开(公告)日：2020-01-21
申请号：US13946386
申请日：2013-07-19
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC: G06N20/00
Abstract: In one embodiment, techniques are shown and described relating to traffic-based inference of influence domains in a network by using learning machines. In particular, in one embodiment, a management device computes a time-based traffic matrix indicating traffic between pairs of transmitter and receiver nodes in a computer network, and also determines a time-based quality parameter for a particular node in the computer network. By correlating the time-based traffic matrix and time-based quality parameter for the particular node, the device may then determine an influence of particular traffic of the traffic matrix on the particular node.
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公开(公告)号：US10536344B2
公开(公告)日：2020-01-14
申请号：US15996645
申请日：2018-06-04
Applicant: Cisco Technology, Inc.
Abstract: In one embodiment, a network assurance service executing in a local network clusters measurements obtained from the local network regarding a plurality of devices in the local network into measurement clusters. The network assurance service computes aggregated metrics for each of the measurement clusters. The network assurance service sends a machine learning model computation request to a remote service outside of the local network that includes the aggregated metrics for each of the measurement clusters. The remote service uses the aggregated metrics to train a machine learning-based model to analyze the local network. The network assurance service receives the trained machine learning-based model to analyze performance of the local network. The network assurance service uses the receive machine learning-based model to analyze performance of the local network.
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