Detecting bug patterns across evolving network software versions

    公开(公告)号:US10805185B2

    公开(公告)日:2020-10-13

    申请号:US15896183

    申请日:2018-02-14

    Abstract: In one embodiment, a network assurance service that monitors a network receives telemetry data regarding monitored characteristics of the network. The service identifies, using a machine learning-based pattern analyzer, a pattern of the monitored characteristics that are associated with failures experienced by one or more networking devices in the network. The service groups networking devices by software version. The service determines probabilities of the pattern being observed concurrently with failures of the grouped network networking devices. A particular probability is associated with a particular group of the networking devices executing a particular software version. The service provides, based on the determined probabilities, data regarding the identified pattern and software versions for display.

    Network configuration change analysis using machine learning

    公开(公告)号:US10680889B2

    公开(公告)日:2020-06-09

    申请号:US15942665

    申请日:2018-04-02

    Abstract: In one embodiment, a network assurance service that monitors one or more networks receives data indicative of networking device configuration changes in the one or more networks. The service also receives one or more performance indicators for the one or more networks. The service trains a machine learning model based on the received data indicative of the networking device configuration changes and on the received one or more performance indicators for the one or more networks. The service predicts, using the machine learning model, a change in the one or more performance indicators that would result from a particular networking device configuration change. The service causes the particular networking device configuration change to be made in the network based on the predicted one or more performance indicators.

    Identifying traffic sensitive interfaces and triggering configuration changes

    公开(公告)号:US12143290B2

    公开(公告)日:2024-11-12

    申请号:US17871142

    申请日:2022-07-22

    Abstract: In one embodiment, a device obtains quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network. The device identifies a correlation between throughput of the particular interface and the quality of experience metrics for the online application. The device makes a determination that the correlation is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics. The device configures, based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.

    SASE pop selection based on client features

    公开(公告)号:US12143289B2

    公开(公告)日:2024-11-12

    申请号:US17712423

    申请日:2022-04-04

    Abstract: In one embodiment, a device obtains client attribute data for clients of an online application that access the online application via a plurality of points of presence in a network. The device forms a performance model that models an application experience metric for the online application as a function of the client attribute data for each of the plurality of points of presence. The device selects, using the performance model, a particular point of presence from among the plurality of points of presence to be used by a particular client to access the online application, based on its client attribute data. The device causes the particular client to access the online application via the particular point of presence selected by the device using the performance model.

    PREDICTIVE APPLICATION-AWARE LOAD-BALANCING BASED ON FAILURE UNCERTAINTY

    公开(公告)号:US20230318977A1

    公开(公告)日:2023-10-05

    申请号:US17712412

    申请日:2022-04-04

    CPC classification number: H04L47/125 H04L47/11 H04L41/5019

    Abstract: In one embodiment, a device obtains metrics for a plurality of network paths via which traffic for an online application may be conveyed. The device models, for each of the plurality of network paths, uncertainty of an application experience metric predicted for the online application across different values of one or more metrics for that path, based on its obtained metrics. The device generates, based on the uncertainty of the application experience metric modeled for each of the plurality of network paths, a load balancing schedule for the plurality of network paths, to maximize the application experience metric for the online application across the plurality of network paths and with a minimal amount of uncertainty. The device causes traffic for the online application to be load balanced across the plurality of network paths in accordance with the load balancing schedule.

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