OBJECTIVE SELECTION FOR LLM-BASED NETWORK TROUBLESHOOTING AND MONITORING AGENTS

    公开(公告)号:US20250148290A1

    公开(公告)日:2025-05-08

    申请号:US18386833

    申请日:2023-11-03

    Abstract: In one implementation, a device receives an input request for a large language model-based troubleshooting agent for a network. The device selects an optimization criterion for the large language model-based troubleshooting agent based on the input request. The device provides the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion. The device sends, to a user interface, an indication of a result of the particular large language model processing the input request.

    CROSS-APPLICATION PREDICTIVE ROUTING
    6.
    发明公开

    公开(公告)号:US20240323112A1

    公开(公告)日:2024-09-26

    申请号:US18678206

    申请日:2024-05-30

    CPC classification number: H04L45/125 H04L45/123 H04L45/302 H04L47/122

    Abstract: In one embodiment, a device predicts, for each of a plurality of applications accessible via a network, quality metrics for different network paths where traffic for that application be routed via one or more paths among the different network paths. The device generates a congestion risk prediction model that predicts a risk of traffic congestion for a particular combination of: applications from among the plurality of applications, traffic flows associated with those applications, and paths among the network paths via which those traffic flows may be routed. The device performs a constrained optimization based on the predicted quality metrics and on the risk of traffic congestion predicted by the model, to assign traffic flows for the applications to a selected subset of the different paths. The device causes the traffic flows to be routed in the network via the selected subset of the different paths to which they are assigned.

    LEARNING PROBING STRATEGIES FOR QOE ASSESSMENT

    公开(公告)号:US20240305542A1

    公开(公告)日:2024-09-12

    申请号:US18117616

    申请日:2023-03-06

    CPC classification number: H04L41/5009 H04L41/16

    Abstract: In one embodiment, a device causes, in accordance with a probing strategy, performance of a probing test by one or more agents in a network and with respect to an online application. The device obtains quality of experience measurements for the online application. The device adjusts, using reinforcement learning, the probing strategy based on how well a predictive model was able to predict the quality of experience measurements given results of the probing test. The device repeats the causing, obtaining, and adjusting steps using the probing strategy adjusted by the device, to find a minimally disruptive probing strategy that provides acceptable performance by the predictive model.

    GENERATING LONG-TERM NETWORK CHANGES FROM SLA VIOLATIONS

    公开(公告)号:US20230027995A1

    公开(公告)日:2023-01-26

    申请号:US17381343

    申请日:2021-07-21

    Abstract: In one embodiment, a device obtains information regarding temporary routing patches applied to a network. Each temporary routing patch implements a routing change in the network for a specified amount of time to avoid or mitigate against a service level agreement violation. The device evaluates, using the information regarding the temporary routing patches applied to the network, a plurality of replay scenarios for the network. The device determines, based on the plurality of replay scenarios, a long-term configuration change for the network. The device provides an indication of the long-term configuration change for display.

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