公开(公告)号：US20140222748A1

公开(公告)日：2014-08-07

申请号：US13946386

申请日：2013-07-19

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Grégory Mermoud ,  Jean-Philippe Vasseur ,  Sukrit Dasgupta

IPC: G06N99/00

CPC classification number: G06N20/00 ,  H04L41/142 ,  H04L41/16 ,  H04L43/0852

Abstract: In one embodiment, techniques are shown and described relating to traffic-based inference of influence domains in a network by using learning machines. In particular, in one embodiment, a management device computes a time-based traffic matrix indicating traffic between pairs of transmitter and receiver nodes in a computer network, and also determines a time-based quality parameter for a particular node in the computer network. By correlating the time-based traffic matrix and time-based quality parameter for the particular node, the device may then determine an influence of particular traffic of the traffic matrix on the particular node.

Abstract translation: 在一个实施例中，通过使用学习机器来显示和描述与网络中的影响域的基于业务的推理有关的技术。 特别地，在一个实施例中，管理设备计算指示计算机网络中的发射机和接收机节点对之间的业务的基于时间的业务矩阵，并且还确定计算机网络中特定节点的基于时间的质量参数。 通过将特定节点的基于时间的业务矩阵和基于时间的质量参数相关联，设备然后可以确定业务矩阵的特定业务对特定节点的影响。

公开(公告)号：US20140222729A1

公开(公告)日：2014-08-07

申请号：US13953113

申请日：2013-07-29

Applicant: Cisco Technology, Inc.

Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Grégory Mermoud

IPC: G06N99/00

CPC classification number: H04L41/14 ,  G06N99/005 ,  H04L41/065 ,  H04L41/5016 ,  H04L41/5035 ,  H04L63/1425 ,  H04L65/40

Abstract: In one embodiment, a state tracking engine (STE) defines one or more classes of elements that can be tracked in a network. A set of elements to track is determined from the one or more classes, and the set of elements is tracked in the network. Access to the tracked set of elements then provided via one or more corresponding application programming interfaces (APIs). In another embodiment, a metric computation engine (MCE) defines one or more network metrics to be tracked in the network. One or more tracked elements are received from the STE. The one or more network metrics are tracked in the network based on the received one or more tracked elements. Access to the tracked network metrics is then provided via one or more corresponding APIs.

Abstract translation: 在一个实施例中，状态跟踪引擎（STE）定义可以在网络中跟踪的一个或多个类别的元件。 要从一个或多个类确定要跟踪的一组元素，并且在网络中跟踪元素集合。 然后通过一个或多个相应的应用程序编程接口（API）提供对跟踪的元素集的访问。 在另一个实施例中，度量计算引擎（MCE）定义了要在网络中跟踪的一个或多个网络度量。 从STE接收到一个或多个跟踪元素。 基于所接收的一个或多个被跟踪的元素，在网络中跟踪一个或多个网络度量。 然后通过一个或多个相应的API提供对跟踪网络度量的访问。

公开(公告)号：US20140219078A1

公开(公告)日：2014-08-07

申请号：US13946268

申请日：2013-07-19

Applicant: Cisco Technology, Inc.

Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Jonathan W. Hui

IPC: H04L12/24 ,  H04L12/751

CPC classification number: H04L45/08 ,  H04L41/0677 ,  H04L41/5009 ,  H04L43/106 ,  H04L45/28

Abstract: In one embodiment, nodes are polled in a network for Quality of Service (QoS) measurements, and a QoS anomaly that affects a plurality of potentially faulty nodes is detected based on the QoS measurements. A path, which traverses the plurality of potentially faulty nodes, is then computed from a first endpoint to a second endpoint. Also, a median node that is located at a point along the path between the first endpoint and the second endpoint is computed. Time-stamped packets are received from the median node, and the first endpoint and the second endpoint of the path are updated based on the received time-stamped packets, such that an amount of potentially faulty nodes is reduced. Then, the faulty node is identified from a reduced amount of potentially faulty nodes.

Abstract translation: 在一个实施例中，在用于服务质量（QoS）测量的网络中轮询节点，并且基于QoS测量来检测影响多个潜在故障节点的QoS异常。 然后，从第一端点到第二端点计算遍历多个潜在故障节点的路径。 此外，计算位于沿着第一端点和第二端点之间的路径的点处的中间节点。 从中间节点接收时间戳的分组，并且基于接收的时间戳分组来更新路径的第一端点和第二端点，使得可能故障节点的量减少。 然后，从减少量的潜在故障节点识别故障节点。

公开(公告)号：US20240381066A1

公开(公告)日：2024-11-14

申请号：US18451556

申请日：2023-08-17

Applicant: Cisco Technology, Inc.

Inventor： Robert Barton ,  Federico Lovison ,  Indermeet Gandhi ,  Jerome Henry ,  Sukrit Dasgupta

IPC: H04W8/02

Abstract: Described herein are devices, systems, methods, and processes for managing roaming actions in a wireless network. The embodiments utilize a machine learning model to generate roaming recommendations based on a plurality of roaming-related metrics. The metrics include data about the current network conditions, the station's previous roaming experiences, and the capabilities of potential roaming target candidates. The roaming recommendations can be provided to a station by an access point (AP). The station can then attempt to perform a roaming action based on the recommendations. After the attempt, the station transmits a roaming feedback to the AP, which includes data about the success or failure of the roaming action and any additional relevant data. In case the station rejects the roaming recommendations, the station may also provide a feedback indicating the rejection. The feedback is utilized to update the machine learning model, thereby improving the accuracy of future roaming recommendations.

公开(公告)号：US10904104B2

公开(公告)日：2021-01-26

申请号：US16393767

申请日：2019-04-24

Applicant: Cisco Technology, Inc.

Inventor： Carlo Zapponi ,  Sukrit Dasgupta

IPC: H04L12/24

Abstract: The technology provides for providing an interactive user interface to explore a complete network, see relationships with various aspects of the network, and drill down to details in an instinctive manner. In some embodiments, network component data is received that identifies metrics associated with network components. A graphical user interface made up of representations of network components of a network is presented, where the network components are selectable. Relevant network components are displayed at varying network scales by receiving an input selecting a first representation of a first network component at a first network level. Based on a network component relationship between the first representation of the first network component and a second relationship of a second network component, second network component data is received that identifies one or more metrics associated with the second network component. The second network component is at a second network level. The one or more metrics associated with the second network component are presented within a context of the second network level.

公开(公告)号：US10764310B2

公开(公告)日：2020-09-01

申请号：US15211231

申请日：2016-07-15

Applicant: Cisco Technology, Inc.

Inventor： Jean-Philippe Vasseur ,  Sukrit Dasgupta ,  Grégory Mermoud

IPC: H04L29/06 ,  G06N20/00

Abstract: In one embodiment, a device in a network receives anomaly data regarding an anomaly detected by a machine learning-based anomaly detection mechanism of a first node in the network. The device matches the anomaly data to threat intelligence feed data from one or more threat intelligence services. The device determines whether to provide threat intelligence feedback to the first node based on the matched threat intelligence feed data and one or more policy rules. The device provides threat intelligence feedback to the first node regarding the matched threat intelligence feed data, in response to determining that the device should provide threat intelligence feedback to the first node.

公开(公告)号：US10757121B2

公开(公告)日：2020-08-25

申请号：US15212588

申请日：2016-07-18

Applicant: Cisco Technology, Inc.

Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Andrea Di Pietro

IPC: H04L29/06 ,  G06N20/00

Abstract: In one embodiment, a device in a network performs anomaly detection functions using a machine learning-based anomaly detector to detect anomalous traffic in the network. The device identifies an ability of one or more nodes in the network to perform at least one of the anomaly detection functions. The device selects a particular one of the anomaly detection functions to offload to a particular one of the nodes, based on the ability of the particular node to perform the particular anomaly detection function. The device instructs the particular node to perform the selected anomaly detection function.

公开(公告)号：US10581901B2

公开(公告)日：2020-03-03

申请号：US15154349

申请日：2016-05-13

Applicant: Cisco Technology, Inc.

Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Andrea Di Pietro

IPC: H04L29/06 ,  H04L12/66 ,  G06N20/00

Abstract: In one embodiment, a primary networking device in a branch network receives a notification of an anomaly detected by a secondary networking device in the branch network. The primary networking device is located at an edge of the network. The primary networking device aggregates the anomaly detected by the secondary networking device and a second anomaly detected in the network into an aggregated anomaly. The primary networking device associates the aggregated anomaly with a location of the secondary networking device in the branch network. The primary networking device reports the aggregated anomaly and the associated location of the secondary networking device to a supervisory device.

公开(公告)号：US10389613B2

公开(公告)日：2019-08-20

申请号：US15872359

申请日：2018-01-16

Applicant: Cisco Technology, Inc.

Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Grégory Mermoud

IPC: H04L12/26

Abstract: In one embodiment, a device in a network receives data indicative of traffic characteristics of traffic associated with a particular application. The device identifies one or more paths in the network via which the traffic associated with the particular application was sent, based on the traffic characteristics. The device determines a probing schedule based on the traffic characteristics. The probing schedule simulates the traffic associated with the particular application. The device sends probes along the one or more identified paths according to the determined probing schedule.

公开(公告)号：US10277476B2

公开(公告)日：2019-04-30

申请号：US14164876

申请日：2014-01-27

Applicant: Cisco Technology, Inc.

Inventor： Grégory Mermoud ,  Jean-Philippe Vasseur ,  Sukrit Dasgupta

IPC: G06N99/00 ,  H04L12/24 ,  H04L12/805 ,  H04L12/751 ,  H04L12/803 ,  H04W24/04 ,  H04L12/721

Abstract: In one embodiment, a predictive model is constructed by mapping multiple network characteristics to multiple network performance metrics. Then, a network performance metric pertaining to a node in a network is predicted based on the constructed predictive model and one or more network characteristics relevant to the node. Also, a local parameter of the node is optimized based on the predicted network performance metric.