Wireless signal strength-based detection of poor network link performance

    公开(公告)号:US12200596B2

    公开(公告)日:2025-01-14

    申请号:US18440575

    申请日:2024-02-13

    Abstract: A cloud-based network management system (NMS) stores path data from network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices. The NMS determines, for a logical path within a specified time window, a wireless signal quality and a link quality based at least in part on the path data. The NMS, in response to determining that the logical path is of a poor link quality, determine a correlation between a poor wireless quality and the poor link quality. The NMS may output a notification that indicates the correlation between the poor wireless quality and the poor link quality of the logical path.

    WIRELESS SIGNAL STRENGTH-BASED DETECTION OF POOR NETWORK LINK PERFORMANCE

    公开(公告)号:US20250150930A1

    公开(公告)日:2025-05-08

    申请号:US19016556

    申请日:2025-01-10

    Abstract: A cloud-based network management system (NMS) stores path data from network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices. The NMS determines, for a logical path within a specified time window, a wireless signal quality and a link quality based at least in part on the path data. The NMS, in response to determining that the logical path is of a poor link quality, determine a correlation between a poor wireless quality and the poor link quality. The NMS may output a notification that indicates the correlation between the poor wireless quality and the poor link quality of the logical path.

    PEER COMPARISON-BASED OUTLIER DETECTION FOR NETWORK PERFORMANCE MONITORING

    公开(公告)号:US20240187337A1

    公开(公告)日:2024-06-06

    申请号:US18440555

    申请日:2024-02-13

    CPC classification number: H04L45/24 H04L43/065 H04L45/123 H04L45/44

    Abstract: Techniques are described for determining one or more outlier logical paths in a computer network. A cloud-based network management system stores path data received from a plurality of network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices for one or more logical paths of a physical interface from the network device over a wide area network (WAN). The network management system compares the path data for the plurality of logical paths to determine one or more outlier logical paths out of the plurality of logical paths. The network management system, in response to determining the one or more outlier logical paths, output a notification indicative of the one or more outlier path data out of the plurality of logical paths.

    AUTODETECTION AND REMEDIATION OF HIGH PROCESSOR USAGE AT NETWORK DEVICES

    公开(公告)号:US20250068507A1

    公开(公告)日:2025-02-27

    申请号:US18774745

    申请日:2024-07-16

    Abstract: A network management system may collect processor usage statistics from one or more network devices. The network management system may determine, for each network device of the one or more network devices, aggregate processor usage statistics across a time window based on the processor usage statistics and, based on an aggregate overall processor usage for a given network device exceeding a baseline threshold, analyze aggregate per-process processor usage for the given network device to determine one or more processes as a root cause of anomalous behavior of the given network device. The network management system may generate a remedial action to remediate the root cause.

    AI-assisted WAN link selection for SD-WAN services

    公开(公告)号:US11991084B2

    公开(公告)日:2024-05-21

    申请号:US17491265

    申请日:2021-09-30

    CPC classification number: H04L47/2425

    Abstract: An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

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