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81. 发明授权

US09215136B2 Aggregated delivery of tunnel fault messages on common ethernet segments 有权
标题翻译：在通用以太网网段上汇总隧道故障消息
公开(公告)号：US09215136B2
公开(公告)日：2015-12-15
申请号：US13928852
申请日：2013-06-27
申请人： Cisco Technology, Inc.
发明人： Joshua B. Littlefield , Sukrit Dasgupta , Hari Rakotoranto
IPC分类号： H04L12/24
CPC分类号： H04L41/0686 , H04L45/28 , H04L45/50
摘要： In one embodiment, a device in a computer network determines one or more tunnels affected by a downstream fault in the computer network, and determines one or more common Ethernet segments of the device used by the affected tunnels. As such, the device generates, for each of the one or more common Ethernet segments, a respective fault message aggregating tunnel information of each of one or more particular affected tunnels on the corresponding common Ethernet segment, and sends each respective fault message with aggregated tunnel information over a selected tunnel of the one or more particular affected tunnels on the corresponding common Ethernet segment.
摘要翻译：在一个实施例中，计算机网络中的设备确定受计算机网络中的下游故障影响的一个或多个隧道，并确定受影响隧道使用的设备的一个或多个公共以太网段。因此，该设备为一个或多个公共以太网段中的每一个生成相应的故障消息，聚合对应的公共以太网段上的一个或多个特定受影响隧道中的每一个的隧道信息，并且将每个相应的故障消息发送到聚合隧道通过相应公共以太网段上的一个或多个特定受影响隧道的选定隧道的信息。

82. 发明申请

US20150195192A1 TRIGGERING REROUTES USING EARLY LEARNING MACHINE-BASED PREDICTION OF FAILURES 有权
标题翻译：使用早期学习机器进行故障预测的触发设备
公开(公告)号：US20150195192A1
公开(公告)日：2015-07-09
申请号：US14164567
申请日：2014-01-27
申请人： CISCO TECHNOLOGY, INC.
发明人： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
IPC分类号： H04L12/703 , H04L12/751 , H04L12/26 , G06F11/20
CPC分类号： H04L45/28 , G06F11/2007 , G06N7/005 , G06N99/005 , H04L41/0836 , H04L41/147 , H04L41/16 , H04L43/0817 , H04L43/0894 , H04L45/02 , H04L45/20 , H04L45/70 , H04W40/18 , H04W40/30 , Y02D70/142 , Y02D70/144 , Y02D70/22 , Y02D70/30
摘要： In one embodiment, network metrics are collected and analyzed in a network having nodes interconnected by communication links. Then, it is predicted whether a network element failure is relatively likely to occur based on the collected and analyzed network metrics. In response to predicting that a network element failure is relatively likely to occur, traffic in the network is rerouted in order to avoid the network element failure before it is likely to occur.
摘要翻译：在一个实施例中，在具有通过通信链路互连的节点的网络中收集和分析网络度量。然后，基于收集和分析的网络度量来预测网元故障是否相对可能发生。响应于预测网络元件故障相对可能发生，网络中的业务被重新路由以避免网元在其可能发生之前发生故障。

83. 发明申请

US20150195176A1 MONITORING NODE LIVENESS IN LOW-POWER LOSSY NETWORKS 审中-公开
标题翻译：监控低功耗网络中的节点生活
公开(公告)号：US20150195176A1
公开(公告)日：2015-07-09
申请号：US14165450
申请日：2014-01-27
申请人： Cisco Technology, Inc.
发明人： Jean-Philippe Vasseur , Grégory Mermoud , Jonathan W. Hui , Sukrit Dasgupta
IPC分类号： H04L12/26 , G06N99/00
CPC分类号： H04L43/12 , H04L41/16 , H04L43/08 , Y04S40/168
摘要： In one embodiment, a learning machine may be used to select observer nodes in a LLN such that the liveness of one or more nodes of interest may be monitored indirectly. In particular, a management device may receive network data on one or more network traffic parameters of a computer network. The management device may then determine, based on the network data, a candidate list of potential observer nodes to monitor activity or inactivity of one or more subject nodes. The management device may then dynamically select, using a machine learning model, a set of optimized observer nodes from the candidate list of potential observer nodes.
摘要翻译：在一个实施例中，可以使用学习机器来选择LLN中的观察者节点，使得可以间接地监视感兴趣的一个或多个节点的活动性。具体地，管理设备可以接收计算机网络的一个或多个网络业务参数上的网络数据。然后，管理设备可以基于网络数据来确定潜在观察者节点的候选列表，以监视一个或多个主题节点的活动或不活动。然后，管理设备可以使用机器学习模型从潜在的观察者节点的候选列表中动态地选择一组优化的观察者节点。

84. 发明申请

US20140281670A1 PROVIDING A BACKUP NETWORK TOPOLOGY WITHOUT SERVICE DISRUPTION 有权
标题翻译：提供无服务中断的备用网络拓扑
公开(公告)号：US20140281670A1
公开(公告)日：2014-09-18
申请号：US13839053
申请日：2013-03-15
申请人： CISCO TECHNOLOGY, INC.
发明人： Jean-Philippe Vasseur , Sukrit Dasgupta
IPC分类号： G06F11/20
CPC分类号： G06F11/2002 , H04L45/02 , H04L45/04 , H04L45/14 , H04L45/22 , H04L45/28 , H04L45/48 , H04W40/24 , H04W40/30 , H04W84/18
摘要： In one embodiment, a primary root node may detect one or more neighboring root nodes based on information received from a first-hop node and may select a backup root node among the neighboring root nodes. Once selected, the backup root node may send the primary root node a networking identification and a corresponding group mesh key which the primary root node may forward to the first-hop nodes to cause the first-hop nodes to migrate to the backup root node when connectivity to the primary root node fails. In addition, the first-hop root nodes may migrate back to the primary root node when connectivity to the primary root node is restored.
摘要翻译：在一个实施例中，主根节点可以基于从第一跳节点接收的信息来检测一个或多个相邻根节点，并且可以在相邻根节点中选择备份根节点。一旦被选择，备份根节点可以向主根节点发送组网标识和相应的组网格密钥，主基本节点可以转发到第一跳节点，以使第一跳节点迁移到备份根节点，当与主根节点的连接失败。此外，当恢复与主根节点的连接时，第一跳根节点可能会迁移回主根节点。

85. 发明申请

US20140222728A1 TRIGGERING ON-THE-FLY REQUESTS FOR SUPERVISED LEARNING OF LEARNING MACHINES 有权
标题翻译：用于监督学习机器的飞行要求
公开(公告)号：US20140222728A1
公开(公告)日：2014-08-07
申请号：US13937705
申请日：2013-07-09
申请人： Cisco Technology, Inc.
发明人： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , H04L67/12 , H04W4/70
摘要： In one embodiment, network data is received at a Learning Machine (LM) in a network. It is determined whether the LM recognizes the received network data based on information available to the LM. When the LM fails to recognize the received network data: a connection to a central management node is established, a request is sent for information relating to the unrecognized network data to the central management node, and information is received from the central management node in response to the request. The received information assists the LM in recognizing the unrecognized network data.
摘要翻译：在一个实施例中，在网络中的学习机器（LM）处接收网络数据。基于LM可用的信息确定LM是否识别接收到的网络数据。当LM无法识别接收到的网络数据时：建立到中央管理节点的连接，向中央管理节点发送与无法识别的网络数据相关的信息的请求，响应于从中央管理节点接收信息要求。接收到的信息有助于LM识别无法识别的网络数据。

86. 发明申请

US20140222727A1 ENHANCING THE RELIABILITY OF LEARNING MACHINES IN COMPUTER NETWORKS 审中-公开
标题翻译：提高计算机网络学习机器的可靠性
公开(公告)号：US20140222727A1
公开(公告)日：2014-08-07
申请号：US13937664
申请日：2013-07-09
申请人： Cisco Technology, Inc.
发明人： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
IPC分类号： G06N99/00 , G06F11/00
CPC分类号： G06N20/00
摘要： In one embodiment, network data is processed using a Learning Machine (LM) algorithm in a network, and results of the processing of network data are determined. A reliability checking algorithm is selected to determine a reliability level of the results. The reliability checking algorithm may be a local reliability checking algorithm or an external reliability checking algorithm. The reliability level of the results is determined using the reliability checking algorithm. Then, the LM algorithm is adjusted based on the determined reliability level.
摘要翻译：在一个实施例中，使用网络中的学习机器（LM）算法处理网络数据，并确定网络数据的处理结果。选择可靠性检查算法来确定结果的可靠性水平。可靠性检查算法可以是局部可靠性检查算法或外部可靠性检查算法。使用可靠性检查算法确定结果的可靠性水平。然后，基于确定的可靠性水平来调整LM算法。

87. 发明申请

US20140222726A1 ACCELERATING LEARNING BY SHARING INFORMATION BETWEEN MULTIPLE LEARNING MACHINES 有权
标题翻译：通过多种学习机器之间的共享信息来加速学习
公开(公告)号：US20140222726A1
公开(公告)日：2014-08-07
申请号：US13937631
申请日：2013-07-09
申请人： Cisco Technology, Inc.
发明人： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , H04L29/12 , H04L61/00
摘要： In one embodiment, variables maintained by each of a plurality of Learning Machines (LMs) are determined. The LMs are hosted on a plurality of Field Area Routers (FARs) in a network, and the variables are sharable between the FARs. A plurality of correlation values defining a correlation between the variables is calculated. Then, a cluster of FARs is computed based on the plurality of correlation values, such that the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables.
摘要翻译：在一个实施例中，确定由多个学习机器（LM）中的每一个维护的变量。 LM在网络中托管在多个场区域路由器（FAR）上，变量在FAR之间可共享。计算定义变量之间的相关性的多个相关值。然后，基于多个相关值计算一组FAR，使得聚类FAR与相关变量相关联，并且该群集允许群集FAR共享其各自的变量。

88. 发明授权

US11580449B2 Traffic-based inference of influence domains in a network by using learning machines 有权
公开(公告)号：US11580449B2
公开(公告)日：2023-02-14
申请号：US16708816
申请日：2019-12-10
申请人： Cisco Technology, Inc.
发明人： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC分类号： G06N20/00 , H04L43/0852 , H04L41/16 , H04L41/142
摘要： 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.

89. 发明授权

US11038775B2 Machine learning-based client selection and testing in a network assurance system 有权
公开(公告)号：US11038775B2
公开(公告)日：2021-06-15
申请号：US16100451
申请日：2018-08-10
申请人： Cisco Technology, Inc.
发明人： Santosh Ghanshyam Pandey , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC分类号： H04L12/24 , H04L12/26 , H04W24/06 , H04B17/318 , H04W84/12 , G06N20/00
摘要： In one embodiment, a network assurance service that monitors a network detects a network anomaly in the network using a machine learning-based anomaly detector. The network assurance service identifies a set of network conditions associated with the detected network anomaly. The network assurance service initiates a network test on one or more clients in the network that exhibit the identified network conditions. The network assurance service retrains the machine learning-based anomaly detector based on a result of the network test.

90. 发明申请

US20200052981A1 MACHINE LEARNING-BASED CLIENT SELECTION AND TESTING IN A NETWORK ASSURANCE SYSTEM 审中-公开
公开(公告)号：US20200052981A1
公开(公告)日：2020-02-13
申请号：US16100451
申请日：2018-08-10
申请人： Cisco Technology, Inc.
发明人： Santosh Ghanshyam Pandey , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC分类号： H04L12/24 , H04L12/26 , H04B17/318 , H04W24/06
摘要： In one embodiment, a network assurance service that monitors a network detects a network anomaly in the network using a machine learning-based anomaly detector. The network assurance service identifies a set of network conditions associated with the detected network anomaly. The network assurance service initiates a network test on one or more clients in the network that exhibit the identified network conditions. The network assurance service retrains the machine learning-based anomaly detector based on a result of the network test.

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