Globally avoiding simultaneous reroutes in a network

    公开(公告)号:US11368401B1

    公开(公告)日:2022-06-21

    申请号:US17153633

    申请日:2021-01-20

    Abstract: In one embodiment, a device obtains, from a plurality of routers in a network, a set of routing patches 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 patches will cause congestion along paths with overlapping path segments. The device adjusts, based on the prediction, the set of routing patches, to avoid causing the congestion.

    CONGESTION DETECTION USING MACHINE LEARNING ON ARBITRARY END-TO-END PATHS

    公开(公告)号:US20220191142A1

    公开(公告)日:2022-06-16

    申请号:US17122755

    申请日:2020-12-15

    Abstract: In one embodiment, a device predicts a range of bitrates expected to be required by one or more applications associated with traffic conveyed via a particular path in a network. The device obtains telemetry data indicative of observed bitrates associated with the traffic conveyed via the particular path in the network. The device identifies, a presence of congestion along the particular path in the network, by comparing the observed bitrates to the range of bitrates expected to be required by the one or more applications. The device causes at least a portion of the traffic to be re-routed from the particular path to a second path in the network, when the device identifies the presence of congestion along the particular path.

    Flash classification using machine learning for device classification systems

    公开(公告)号:US11349716B2

    公开(公告)日:2022-05-31

    申请号:US16878780

    申请日:2020-05-20

    Abstract: In various embodiments, a device classification service makes a determination that an endpoint device in a network is eligible for expedited device classification based on a policy. The device classification service obtains, after making the determination that the endpoint device in the network is eligible for expedited device classification, telemetry data regarding the endpoint device generated by actively probing the endpoint device. The device classification service determines whether the telemetry data regarding the endpoint device matches any existing device classification rules. The device classification service generates, based on the telemetry data, a device classification rule that assigns a device type to the endpoint device, when the telemetry data does not match any existing device classification rules.

    Merging and optimizing heterogeneous rulesets for device classification

    公开(公告)号:US11232372B2

    公开(公告)日:2022-01-25

    申请号:US16185086

    申请日:2018-11-09

    Abstract: In one embodiment, a device classification service receives a plurality of device classification rulesets, each ruleset associating a set of device characteristics with a device type label. The device classification service forms a unified ruleset by resolving a conflict between conflicting device characteristics from two or more of the device classification rulesets. The device classification service trains a machine learning-based device classifier using the unified ruleset. The device classification service classifies, using telemetry data for a device in a network as input to the trained device classifier, the device with the device type label.

    Privacy-aware model generation for hybrid machine learning systems

    公开(公告)号:US11165656B2

    公开(公告)日:2021-11-02

    申请号:US16697344

    申请日:2019-11-27

    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.

    REVISITING DEVICE CLASSIFICATION RULES UPON OBSERVATION OF NEW ENDPOINT ATTRIBUTES

    公开(公告)号:US20210328986A1

    公开(公告)日:2021-10-21

    申请号:US16854115

    申请日:2020-04-21

    Abstract: In various embodiments, a device classification service uses an initial device classification rule to label each of a set of endpoint devices in a network as being of a particular device type. The device classification service identifies a particular attribute exhibited by at least a portion of the set of endpoint devices and was not previously used to generate the initial device classification rule. The device classification service generates one or more new device classification rules based in part on the particular attribute. The device classification service switches from using the initial device classification rule to label endpoint devices in the network to using the one or more new device classification rules to label endpoint devices in the network.

    Machine learning approach for dynamic adjustment of BFD timers in SD-WAN networks

    公开(公告)号:US11032181B2

    公开(公告)日:2021-06-08

    申请号:US16434263

    申请日:2019-06-07

    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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