DETECTING NETWORK EVENTS HAVING ADVERSE USER IMPACT

    公开(公告)号:US20240291743A1

    公开(公告)日:2024-08-29

    申请号:US18656206

    申请日:2024-05-06

    CPC classification number: H04L43/0876 H04L41/0631 H04L43/062 H04L45/48

    Abstract: A method includes receiving, by a network management system, network data from a plurality of network devices configured to provide a network at a site; receiving, by the processing circuitry, user impact data from a plurality of client devices that access the network at the site; determining, based on the network data, a pattern of one or more network events occurring over time; correlating in time the pattern of the one or more network events to an adverse user impact event indicated by the user impact data received from the plurality of client devices; and determining, in response to the correlating, an instance of overwhelming network traffic having an adverse user impact. In some examples, the network data includes network traffic impact data, such as a number of packets dropped at a switch port due to congestion.

    Identifying root cause of failures through detection of network scope failures

    公开(公告)号:US11838172B2

    公开(公告)日:2023-12-05

    申请号:US17446601

    申请日:2021-08-31

    CPC classification number: H04L41/0631 H04W24/04

    Abstract: Techniques are described by which a network management system (NMS) is configured to provide identification of root cause failure through the detection of network scope failures. For example, the NMS comprises one or more processors; and a memory comprising instructions that when executed by the one or more processors cause the one or more processors to: generate a hierarchical attribution graph comprising attributes representing different network scopes at different hierarchical levels; receive network event data, wherein the network event data is indicative of operational behavior of the network, including one or more of successful events or one or more failure events associated with one or more client devices; and apply a machine learning model to the network event data and to a particular network scope in the hierarchical attribution graph to detect whether the particular network scope has failure.

    Log analysis in vector space
    5.
    发明授权

    公开(公告)号:US11815987B2

    公开(公告)日:2023-11-14

    申请号:US17448108

    申请日:2021-09-20

    Abstract: The disclosed embodiments provide for identification of a remedial action based on analysis of a system log file. In some example embodiments, messages from the system log file are used as input to generate vectors within a vector space. Portions of the log messages may generate vectors that cluster into a region in the vector space. The region of vector space is associated with one or more remedial actions. The disclosed embodiments are configured, in some example embodiments, to perform the one or more remedial actions when activity in the log file maps to the region of vector space associated with the one or more remedial actions. In some example embodiments, a remedial action can include submitting a problem report to a problem tracking database.

    APPLICATION RECORDS USING SESSION INFORMATION

    公开(公告)号:US20230336446A1

    公开(公告)日:2023-10-19

    申请号:US18336130

    申请日:2023-06-16

    CPC classification number: H04L43/067 H04L43/062 H04L45/306 H04L67/14

    Abstract: Techniques are disclosed for the identification of applications from communication sessions of network traffic between client devices and the generation of application-specific metrics for network traffic associated with the applications. In one example, a router obtains metrics for a plurality of packets. The router determines a session of a plurality of sessions associated with each packet. For each determined session, the router generates metrics for the session from the metrics of the packets associated with the session and determines an application of a plurality of applications associated with the session. For each determined application, the router generates metrics for the application from the metrics of the sessions associated with the application and transmits, to a device, the metrics for the application. In some examples, the router generates the metrics for each application on a per-client, per-next-hop, or per-traffic class basis.

    Adaptive log data level in a computing system

    公开(公告)号:US11494255B2

    公开(公告)日:2022-11-08

    申请号:US17200229

    申请日:2021-03-12

    Inventor: Jisheng Wang

    Abstract: Disclosed are embodiments for improving remote diagnostics of a computer system. Some embodiments obtain operational parameter values and log data from a plurality of network devices, and provide the operational parameter values and log data to a machine learning model. The model is trained to identify a root cause of a degradation of the computer system based on the operational parameter values and log data, and to provide recommendations of log data level settings for the network devices. If the model identifies a root cause of the degradation with sufficient confidence, a remedial action is identified and applied to the computer system. If the confidence level is insufficient, log data level settings of the network devices are modified based on the recommendations of the model. This process is performed iteratively, for example, such that the model receives log data based on its recommended log data levels, until a root cause is identified with sufficient confidence.

    ADAPTIVE LOG DATA LEVEL IN A COMPUTING SYSTEM

    公开(公告)号:US20220291989A1

    公开(公告)日:2022-09-15

    申请号:US17200229

    申请日:2021-03-12

    Inventor: Jisheng Wang

    Abstract: Disclosed are embodiments for improving remote diagnostics of a computer system. Some embodiments obtain operational parameter values and log data from a plurality of network devices, and provide the operational parameter values and log data to a machine learning model. The model is trained to identify a root cause of a degradation of the computer system based on the operational parameter values and log data, and to provide recommendations of log data level settings for the network devices. If the model identifies a root cause of the degradation with sufficient confidence, a remedial action is identified and applied to the computer system. If the confidence level is insufficient, log data level settings of the network devices are modified based on the recommendations of the model. This process is performed iteratively, for example, such that the model receives log data based on its recommended log data levels, until a root cause is identified with sufficient confidence.

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