Estimating the need for user feedback in training multi-application QoE models

    公开(公告)号:US12160348B1

    公开(公告)日:2024-12-03

    申请号:US18198553

    申请日:2023-05-17

    Abstract: In one embodiment, a device identifies a plurality of online applications accessible via a network for which a prediction model was trained to predict their application experiences. The device makes a determination as to whether a particular online application is behaviorally similar to any of the plurality of online applications. The device obtains, based on the determination, application experience metrics for the particular online application, when the particular online application is not behaviorally similar to any of the plurality of online applications. The device trains, using the application experience metrics for the particular online application, the prediction model to predict an application experience of the particular online application in addition to those of the plurality of online applications, when the particular online application is not behaviorally similar to any of the plurality of online applications.

    ESTIMATING THE NEED FOR USER FEEDBACK IN TRAINING MULTI-APPLICATION QOE MODELS

    公开(公告)号:US20240388507A1

    公开(公告)日:2024-11-21

    申请号:US18198553

    申请日:2023-05-17

    Abstract: In one embodiment, a device identifies a plurality of online applications accessible via a network for which a prediction model was trained to predict their application experiences. The device makes a determination as to whether a particular online application is behaviorally similar to any of the plurality of online applications. The device obtains, based on the determination, application experience metrics for the particular online application, when the particular online application is not behaviorally similar to any of the plurality of online applications. The device trains, using the application experience metrics for the particular online application, the prediction model to predict an application experience of the particular online application in addition to those of the plurality of online applications, when the particular online application is not behaviorally similar to any of the plurality of online applications.

    Actively learning PoPs to probe and probing frequency to maximize application experience predictions

    公开(公告)号:US11909618B2

    公开(公告)日:2024-02-20

    申请号:US17714483

    申请日:2022-04-06

    CPC classification number: H04L43/12 H04L47/2475

    Abstract: In one embodiment, a device computes, for each of a set of points of presence (PoPs) via which traffic for an online application can be sent from a location, application experience metrics predicted for the application over time. The device assigns, for each of the set of PoPs, weights to different time periods, based on measures of uncertainty associated with the predicted application experience metrics. The device generates, based on the weights assigned to the different time periods for each of the set of PoPs, schedules for probing network paths connecting the location to the online application via those PoPs. The device causes the network paths to be probed in accordance with their schedules. Results of this probing are used to select a particular PoP from among the set of PoPs via which traffic for the online application should be sent from the location during a certain time period.

    NETWORK LOCALIZATION BASED ON PROBING RESULTS

    公开(公告)号:US20240007387A1

    公开(公告)日:2024-01-04

    申请号:US17853526

    申请日:2022-06-29

    CPC classification number: H04L45/08 H04L43/12

    Abstract: In one embodiment, a device obtains probe results generated by probing a plurality of entities in a network from a first node in the network. The device generates a location identifier for the first node that represents its location in the network, based on the probe results. The device selects a configuration associated with the first node for use by a second node in the network, based on a difference between the location identifier for the first node and a location identifier for the second node. The device causes the configuration to be deployed to the second node.

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