公开(公告)号：US10999146B1

公开(公告)日：2021-05-04

申请号：US16854292

申请日：2020-04-21

Applicant: Cisco Technology, Inc.

Inventor： Pierre-André Savalle ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  David Tedaldi ,  Jürg Nicolaus Diemand ,  Stéphane Bernard Martin

IPC: G06F15/16 ,  H04L12/24 ,  G06F16/35 ,  G06N20/00 ,  H04L12/26

Abstract: In various embodiments, a device classification service forms a device cluster by applying clustering to attributes of endpoint devices observed in one or more networks. The device classification service applies an initial device classification rule to the endpoint devices in the device cluster, based on one or more of the endpoint devices in the device cluster matching the initial device classification rule. The device classification service computes metrics for the initial device classification rule that quantify how well the attributes of the endpoint devices in the device cluster match the initial device classification rule. The device classification service decides, based on the metrics, whether to associate the initial device classification rule with the device cluster or generate a new device classification rule based on the device cluster.

公开(公告)号：US20230164029A1

公开(公告)日：2023-05-25

申请号：US17532452

申请日：2021-11-22

Applicant: Cisco Technology, Inc.

Inventor： Grégory Mermoud ,  Stéphane Bernard Martin ,  Jean-Philippe Vasseur

IPC: H04L41/082 ,  H04L41/0823 ,  H04L41/08 ,  H04L41/16 ,  H04L41/5025

CPC classification number: H04L41/082 ,  H04L41/0823 ,  H04L41/0886 ,  H04L41/16 ,  H04L41/5025

Abstract: In one embodiment, a device associates application performance of an online application with network configuration changes implemented across one or more software-defined networks. The device trains a machine learning model to predict an effect of a configuration change on the application performance for any given portion of the one or more software-defined networks. The device generates a recommended configuration change for a particular portion of the one or more software-defined networks, using the machine learning model. The device causes the recommended configuration change to be implemented in the particular portion of the one or more software-defined networks.