LLM-BASED NETWORK TROUBLESHOOTING USING EXPERT-CURATED RECIPES

    公开(公告)号:US20250150321A1

    公开(公告)日:2025-05-08

    申请号:US18386837

    申请日:2023-11-03

    Abstract: In one implementation, a device receives an input request for a large language model-based network troubleshooting agent regarding an issue in a network. The large language model-based network troubleshooting agent performs a lookup of a recipe based on the input request, wherein the recipe comprises contextual information for the issue. The device generates, by the large language model-based network troubleshooting agent, a prompt for a large language model based on the input request and on the recipe. The device provides, by the large language model-based network troubleshooting agent, the prompt to the large language model to troubleshoot the issue in the network.

    ROOT-CAUSING SAAS ENDPOINTS FOR NETWORK ISSUES IN APPLICATION-DRIVEN PREDICTIVE ROUTING

    公开(公告)号:US20230018772A1

    公开(公告)日:2023-01-19

    申请号:US17379586

    申请日:2021-07-19

    Abstract: In one embodiment, a device obtains telemetry data for network paths to a plurality of servers for an online application. The telemetry data includes application experience metrics based on feedback provided by users of the online application. The device decomposes the telemetry data for the network paths from different vantage points. The device also identifies, using the decomposed telemetry data, a particular endpoint of the online application as a cause of application experience degradation for the online application. The device provides an alert indicative of the particular endpoint of the online application being the cause of quality of experience degradation for the online application.

    QOS CONFIGURATION UPDATE BASED ON BEHAVIORAL APPLICATION CORRELATION

    公开(公告)号:US20220353181A1

    公开(公告)日:2022-11-03

    申请号:US17242708

    申请日:2021-04-28

    Abstract: In one embodiment, a device obtains behavioral metrics for application traffic in a network for a plurality of applications. The device identifies a first application and a second application from among the plurality of applications as fate sharing applications, based on a correlation between the behavioral metrics for their application traffic. The device generates a configuration change for the network that would prevent the first application and the second application from being fate sharing applications, when application traffic for the first application negatively affects the behavioral metrics for the application traffic of the second application. The device causes the configuration change to be implemented in the network.

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