公开(公告)号：US20240323112A1

公开(公告)日：2024-09-26

申请号：US18678206

申请日：2024-05-30

Applicant: Cisco Technology, Inc.

Inventor： Grégory MERMOUD ,  Grégoire MAGENDIE ,  Jean-Philippe VASSEUR

IPC: H04L45/125 ,  H04L45/12 ,  H04L45/302 ,  H04L47/122

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.

公开(公告)号：US20240388515A1

公开(公告)日：2024-11-21

申请号：US18198080

申请日：2023-05-16

Applicant: Cisco Technology, Inc.

Inventor： Grégory MERMOUD ,  Petar STUPAR ,  Grégoire MAGENDIE ,  Jean-Philippe VASSEUR

IPC: H04L41/147 ,  H04L41/22 ,  H04L43/08

Abstract: In one embodiment, a device obtains a plurality of telemetry metrics regarding an online application accessed via a network. The device trains, based on the plurality of telemetry metrics, a gradient boosted tree-based prediction model to make predictions regarding a quality of experience for the online application. The device quantifies how influential a particular telemetry metric is on the predictions. The device provides a visualization tool for display that indicates how influential the particular telemetry metric is on the predictions.

公开(公告)号：US20240406078A1

公开(公告)日：2024-12-05

申请号：US18205247

申请日：2023-06-02

Applicant: Cisco Technology, Inc.

Inventor： Pierre-André Savalle ,  Jean-Philippe VASSEUR ,  Grégory MERMOUD ,  Andrea DI PIETRO ,  Grégoire MAGENDIE

IPC: H04L41/5025 ,  H04L41/0816 ,  H04L41/5067 ,  H04L43/04 ,  H04L67/75

Abstract: In one embodiment, a device makes, using a prediction model, a prediction regarding a quality of experience metric for an online application, based on telemetry from a network used to access the network. The device determines a degree of uncertainty associated with the prediction. The device selects one or more users of the online application from which user feedback should be requested, based on the degree of uncertainty. The device requests that the one or more users provide user feedback regarding their satisfaction with the online application.

公开(公告)号：US20240259281A1

公开(公告)日：2024-08-01

申请号：US18103040

申请日：2023-01-30

Applicant: Cisco Technology, Inc.

Inventor： Grégory Mermoud ,  Pierre-André SAVALLE ,  Jean-Philippe VASSEUR ,  Grégoire MAGENDIE ,  Michal Wladyslaw GARCARZ

IPC: H04L41/5067 ,  G06F16/95

CPC classification number: H04L41/5067 ,  G06F16/95

Abstract: In one embodiment, a device obtains browser waterfall data from a web browser of a client that is used to access an online application via a network. The device obtains user feedback from the client indicative of whether a user of the client is satisfied with their experience with the online application. The device trains, using the browser waterfall data and user feedback as training data, a prediction model to predict a quality of experience metric for the online application. The device causes an adjustment to the network based on a prediction by the prediction model.

公开(公告)号：US20250158895A1

公开(公告)日：2025-05-15

申请号：US18388242

申请日：2023-11-09

Applicant: Cisco Technology, Inc.

Inventor： Grégory Mermoud ,  Pierre-André SAVALLE ,  Grégoire MAGENDIE ,  Jean-Philippe VASSEUR ,  Eduard SCHORNIG

IPC: H04L41/16 ,  H04L41/22

Abstract: In one implementation, a device uses a large language model-based agent to complete a task with respect to a computer network. The device makes an evaluation of an action taken by the large language model-based agent to complete the task. The device selects, based in part on the evaluation, a set of allowed actions that the large language model-based agent is allowed to perform to complete the task. The device updates the large language model-based agent with an indication of the set of allowed actions that it is allowed to perform to complete the task.

公开(公告)号：US20240291715A1

公开(公告)日：2024-08-29

申请号：US18660598

申请日：2024-05-10

Applicant: Cisco Technology, Inc.

Inventor： Grégory MERMOUD ,  Jean-Philippe VASSEUR ,  Grégoire MAGENDIE ,  Romain KAKKO-CHILOFF

IPC: H04L41/08 ,  G06N5/022 ,  H04L67/306

CPC classification number: H04L41/0883 ,  G06N5/022 ,  H04L67/306

Abstract: In one embodiment, a recommendation service of a device provides a recommended action to a client of an online application predicted to improve a quality of experience metric for the online application. The device receives feedback from the client indicative of the recommended action not being implemented by a user of the client. The device determines, based on the feedback, a reason for the recommended action not being implemented. The device updates the recommendation service based on the reason for the recommended action not being implemented.

公开(公告)号：US20240007389A1

公开(公告)日：2024-01-04

申请号：US17853568

申请日：2022-06-29

Applicant: Cisco Technology, Inc.

Inventor： Grégory Mermoud ,  Grégoire MAGENDIE ,  Jean-Philippe VASSEUR

IPC: H04L45/125 ,  H04L45/12 ,  H04L45/302 ,  H04L47/122

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

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.