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公开(公告)号：US20160020969A1
公开(公告)日：2016-01-21
申请号：US14336106
申请日：2014-07-21
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: H04L43/062 , H04L12/2854 , H04L41/0681 , H04L43/0852 , H04L43/12 , H04L43/16 , H04L63/00 , H04L63/1425
Abstract: In one embodiment, a network device routes traffic along a network path and receives a performance threshold crossing alert regarding performance of the network path. The network device detects that the performance threshold crossing alert is part of a potential network attack by analyzing, by the device, the performance threshold crossing alert. The network device also provides a notification of the detected network attack.
Abstract translation: 在一个实施例中，网络设备沿着网络路径路由业务，并且接收关于网络路径的性能的性能阈值交叉警报。 网络设备通过由设备分析性能阈值跨越警报来检测性能阈值交叉警报是潜在网络攻击的一部分。 网络设备还提供检测到的网络攻击的通知。
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公开(公告)号：US20150333953A1
公开(公告)日：2015-11-19
申请号：US14276501
申请日：2014-05-13
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta
IPC: H04L12/24 , H04L12/707 , H04L12/26
CPC classification number: H04L41/0668 , H04L43/062 , H04L43/0876 , H04L45/22 , H04L47/122 , H04L47/125 , H04L47/127
Abstract: In one embodiment, a plurality of paths in a network from a source device to a destination device is identified. A predicted performance for packet delivery along a primary path from the plurality of paths is determined. The predicted performance for packet delivery along the primary path is then compared to a performance threshold. Traffic sent along the primary path may be duplicated onto a backup path selected from the plurality of paths based on a determination that the predicted performance along the primary path is below the performance threshold.
Abstract translation: 在一个实施例中，识别从源设备到目的地设备的网络中的多个路径。 确定沿着从多个路径的主要路径的分组递送的预测性能。 然后将沿着主路径的分组传送的预测性能与性能阈值进行比较。 基于沿着主路径的预测性能低于性能阈值的确定，沿着主路径发送的业务可以被复制到从多个路径中选择的备份路径上。
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公开(公告)号：US20150332155A1
公开(公告)日：2015-11-19
申请号：US14280082
申请日：2014-05-16
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: G06N99/005 , G06N5/048
Abstract: In one embodiment, a network device receives metrics regarding a path in the network. A predictive model is generated using the received metrics and is operable to predict available bandwidth along the path for a particular type of traffic. A determination is made as to whether a confidence score for the predictive model is below a confidence threshold associated with the particular type of traffic. The device obtains additional data regarding the path based on a determination that the confidence score is below the confidence threshold. The predictive model is updated using the additional data regarding the path.
Abstract translation: 在一个实施例中，网络设备接收关于网络中的路径的度量。 使用所接收的度量来生成预测模型，并且可操作地预测用于特定类型的业务的沿着路径的可用带宽。 确定预测模型的置信度得分是否低于与特定类型的业务相关联的置信度阈值。 基于置信度得分低于置信度阈值的确定，该装置获得关于路径的附加数据。 使用关于路径的附加数据更新预测模型。
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54.发明申请LEARNING END-TO-END DELAYS IN COMPUTER NETWORKS FROM SPORADIC ROUND-TRIP DELAY PROBING 有权从SPORADIC循环延迟探测计算机网络学习端到端延迟公开(公告)号：US20150195171A1
公开(公告)日：2015-07-09
申请号：US14164781
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Grêgory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: H04L43/0864 , G06N7/005 , G06N99/005 , H04L41/142 , H04L41/147 , H04L41/16 , H04L43/10 , H04L43/106 , H04W24/10
Abstract: In one embodiment, periodic round-trip probes are executed in a network, whereby a packet is transmitted along a particular communication path from a source to a destination and back to the source. Statistical information relating to the round-trip probes is gathered, and a transmission delay of the round-trip probes is calculated based on the gathered statistical information. Also, an end-to-end transmission delay along an arbitrary communication path in the network is estimated based on the calculated transmission delay of the round-trip probes.
Abstract translation: 在一个实施例中，在网络中执行周期性的往返探测器，由此，分组沿着特定的通信路径从源传输到目的地并返回到源。 收集与往返探测有关的统计信息，并根据收集的统计信息计算往返探测器的传输延迟。 此外，基于所计算的往返探测的传输延迟来估计沿着网络中的任意通信路径的端到端传输延迟。
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55.发明申请OPTIMIZING NETWORK PARAMETERS BASED ON A LEARNED NETWORK PERFORMANCE MODEL 审中-公开基于有针对性的网络性能模型优化网络参数公开(公告)号：US20150195136A1
公开(公告)日：2015-07-09
申请号：US14164876
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: H04L41/5025 , G06N99/00 , G06N99/005 , H04L41/12 , H04L43/0852 , H04L45/02 , H04L45/70 , H04L47/122 , H04L47/365 , H04W24/02 , H04W24/04 , Y04S40/164
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.
Abstract translation: 在一个实施例中，通过将多个网络特性映射到多个网络性能度量来构建预测模型。 然后，基于所构建的预测模型和与节点相关的一个或多个网络特性来预测与网络中的节点有关的网络性能度量。 此外，基于预测的网络性能度量来优化节点的本地参数。
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Patent Agency Ranking
56.发明申请POINT-TO-MULTIPOINT COMMUNICATION INFRASTRUCTURE FOR EXPERT-BASED KNOWLEDGE FEED-BACK USING LEARNING MACHINES 有权基于专家知识的点对多点通信基础架构回馈使用学习机公开(公告)号：US20150186799A1
公开(公告)日：2015-07-02
申请号：US14165462
申请日：2014-01-27
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Sukrit Dasgupta , Jean-Philippe Vasseur , Grégory Mermoud
IPC: G06N99/00
CPC classification number: G06N99/005
Abstract: In one embodiment, techniques are shown and described relating to a point-to-multipoint communication infrastructure for expert-based knowledge feed-back using learning machines. A learning machine may communicate an expert discovery request into a network to discover one or more experts, and then receive from the one or more experts, one or more expert discovery responses. Based on the one or more received expert discovery responses, the learning machine may then build a dynamic multicast tree of experts to assist the learning machine in a computer network.
Abstract translation: 在一个实施例中，与使用学习机器的基于专家的知识反馈的点对多点通信基础设施相关的技术被示出和描述。 学习机器可以将专家发现请求传达到网络中以发现一个或多个专家，然后从一个或多个专家接收一个或多个专家发现响应。 基于一个或多个接收到的专家发现响应，学习机器然后可以构建专家的动态多播树，以帮助学习机器在计算机网络中。
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57.发明申请LEARNING DATA PROCESSOR FOR DISTRIBUTING LEARNING MACHINES ACROSS LARGE-SCALE NETWORK INFRASTRUCTURES 有权学习数据处理器，用于分布大规模网络基础设施的学习机器公开(公告)号：US20150186798A1
公开(公告)日：2015-07-02
申请号：US14163638
申请日：2014-01-24
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
IPC: G06N99/00
CPC classification number: G06N99/005 , G06Q10/0631 , G06Q10/06375 , H04L12/185 , H04L41/145 , H04L41/147 , H04L41/16 , H04L45/48 , H04L47/2483 , H04L67/26
Abstract: In one embodiment, a learning data processor determines a plurality of machine learning features in a computer network to collect. Upon receiving data corresponding to the plurality of features, the learning data processor may aggregate the data, and pushes the aggregated data for select features to interested learning machines associated with the computer network.
Abstract translation: 在一个实施例中，学习数据处理器确定要收集的计算机网络中的多个机器学习特征。 在接收到与多个特征相对应的数据时，学习数据处理器可以聚合数据，并将用于选择特征的聚合数据推送到与计算机网络相关联的感兴趣的学习机器。
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58.发明申请公开(公告)号：US20150092538A1
公开(公告)日：2015-04-02
申请号：US14039191
申请日：2013-09-27
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta , Grégory Mermoud
CPC classification number: H04L45/24 , H04L43/08 , H04L45/308
Abstract: In one embodiment, a plurality of communication paths in a second direction in a communication network is determined, based on reversing communication paths established in a first direction in the communication network. Then, a path quality of the communication paths in the second direction is monitored. Based on the monitored path quality, it is then determined whether the communication paths in the second direction satisfy a communication requirement. Finally, a particular communication path of unacceptable quality in the second direction is detected when the particular communication path in the second direction fails to satisfy the communication requirement.
Abstract translation: 在一个实施例中，基于在通信网络中沿第一方向建立的反向通信路径来确定在通信网络中的第二方向上的多个通信路径。 然后，监视第二方向上的通信路径的路径质量。 基于所监视的路径质量，确定第二方向的通信路径是否满足通信要求。 最后，当第二方向上的特定通信路径不能满足通信要求时，检测到在第二方向上具有不可接受质量的特定通信路径。
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公开(公告)号：US08984277B2
公开(公告)日：2015-03-17
申请号：US13631106
申请日：2012-09-28
Applicant: Cisco Technology, Inc.
Inventor： Sukrit Dasgupta , Jean-Philippe Vasseur
IPC: H04L29/06 , H04L12/715
CPC classification number: H04L47/2475 , H04L45/64 , H04L63/062 , H04L63/0853 , H04L63/0884 , H04L67/12
Abstract: In one embodiment, a capable node in a low power and lossy network (LLN) may monitor the authentication time for one or more nodes in the LLN. The capable node may dynamically correlate the authentication time with the location of the one or more nodes in the LLN in order to identify one or more authentication-delayed nodes. The node may then select, based on the location of the one or more authentication-delayed nodes, one or more key-delegation nodes to receive one or more network keys so that the key-delegation nodes may perform localized authentication of one or more of the authentication-delayed nodes. The capable node may then distribute the one or more network keys to the one or more key-delegation nodes.
Abstract translation: 在一个实施例中，低功率和有损网络（LLN）中的能力节点可以监视LLN中的一个或多个节点的认证时间。 能力节点可以将认证时间与LLN中的一个或多个节点的位置动态相关，以便识别一个或多个认证延迟节点。 然后，节点可以基于一个或多个认证延迟的节点的位置来选择一个或多个密钥委派节点来接收一个或多个网络密钥，使得密钥委派节点可以执行一个或多个 认证延迟节点。 有能力的节点可以然后将一个或多个网络密钥分配给一个或多个密钥委派节点。
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60.发明申请HIDDEN MARKOV MODEL BASED ARCHITECTURE TO MONITOR NETWORK NODE ACTIVITIES AND PREDICT RELEVANT PERIODS 审中-公开隐藏MARKOV模型建筑监测网络节点活动和预测相关周期公开(公告)号：US20140222997A1
公开(公告)日：2014-08-07
申请号：US13955648
申请日：2013-07-31
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: H04L41/142 , H04L41/147 , H04L41/16 , H04L43/045 , H04L43/065
Abstract: In one embodiment, techniques are shown and described relating to a Hidden Markov Model based architecture to monitor network node activities and predict relevant periods. In particular, in one embodiment, a device determines a statistical model for each of one or more singular-node traffic profiles (e.g., based on one or more Hidden Markov Models (HMMs) each corresponding to a respective one of the one or more traffic profiles). By analyzing respective traffic from individual nodes in a computer network, and matching the respective traffic against the statistical model for the one or more traffic profiles, the device may detecting a matching traffic profile for the individual nodes in a computer network. In addition, the device may predict relevant periods of traffic for the individual nodes by extrapolating a most-likely future sequence based on prior respective traffic of the individual nodes and the corresponding matching traffic profile.
Abstract translation: 在一个实施例中，显示和描述与基于隐马尔可夫模型的架构相关的技术来监视网络节点活动并预测相关周期。 特别地，在一个实施例中，设备确定一个或多个奇异节点业务简档中的每一个的统计模型（例如，基于一个或多个隐马尔可夫模型（HMM），每个隐马尔可夫模型（HMM）对应于一个或多个业务中的相应一个 档案）。 通过分析来自计算机网络中的各个节点的相应流量，并且将相应流量与针对一个或多个流量简档的统计模型相匹配，该设备可以检测计算机网络中各个节点的匹配流量简档。 此外，设备可以基于各个节点的先前相应业务和对应的匹配流量简档来推测出最可能的将来序列来预测各个节点的相关时段。
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