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公开(公告)号：WO2021194717A1
公开(公告)日：2021-09-30
申请号：PCT/US2021/020880
申请日：2021-03-04
Applicant: CISCO TECHNOLOGY, INC.
Inventor： DI PIETRO, Andrea , DASGUPTA, Sukrit
Abstract: The present technology allows a hybrid approach to using artificial intelligence engines to perform issue generation, leveraging both on-premise and cloud components. In the technology, a cloud-based computing device receives data associated with a computing network of devices and uses machine- learning to create a model of the computing network. The cloud-based computing device communicates the model to a computing system located on-premise with the computing network and receives data related to the issues and insights created by the on-premise computing system. The cloud-based computing device determines if the on-premise computing system is producing issues and insights below a threshold quality. If yes, the cloud-based computing device updates the model based on updated data associated with the computing network and communicates the updated model to the on-premise computing system.
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公开(公告)号：WO2016014470A1
公开(公告)日：2016-01-28
申请号：PCT/US2015/041258
申请日：2015-07-21
Applicant: CISCO TECHNOLOGY, INC.
Inventor： VASSEUR, Jean-Philippe , MERMOUD, Grégory , DASGUPTA, Sukrit
CPC classification number: G06N5/048 , G06N99/005 , H04L12/1827
Abstract: In one embodiment, a management system determines respective capability information of machine learning systems, the capability information including at least an action the respective machine learning system is configured to perform. The management system receives, for each of the machine learning systems, respective performance scoring information associated with the respective action, and computes a degree of freedom for each machine learning system to perform the respective action based on the performance scoring information. Accordingly, the management system then specifies the respective degree of freedom to the machine learning systems. In one embodiment, the management system comprises a management device that computes a respective trust level for the machine learning systems based on receiving the respective performance scoring feedback, and a policy engine that computes the degree of freedom based on receiving the trust level. In further embodiments, the machine learning system performs the action based on the degree of freedom.
Abstract translation: 在一个实施例中，管理系统确定机器学习系统的相应能力信息，所述能力信息至少包括相应的机器学习系统被配置为执行的动作。 管理系统针对每个机器学习系统接收与相应动作相关联的各自的性能评分信息，并且基于性能评分信息计算每个机器学习系统执行相应动作的自由度。 因此，管理系统然后指定机器学习系统的相应自由度。 在一个实施例中，管理系统包括管理装置，其基于接收相应的性能评分反馈来计算机器学习系统的相应信任级别，以及基于接收信任级别来计算自由度的策略引擎。 在另外的实施例中，机器学习系统基于自由度来执行动作。
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3.发明申请TRAFFIC-BASED INFERENCE OF INFLUENCE DOMAINS IN A NETWORK BY USING LEARNING MACHINES 审中-公开通过使用学习机器在网络中影响流量领域的交通干扰
公开(公告)号：WO2014123920A1
公开(公告)日：2014-08-14
申请号：PCT/US2014/014700
申请日：2014-02-04
Applicant: CISCO TECHNOLOGY, INC.
Inventor： MERMOUD, Gregory , VASSEUR, Jean-philippe , DASGUPTA, Sukrit
CPC classification number: G06N99/005 , 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。 通过将基于时间的业务矩阵和针对特定节点的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。
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公开(公告)号：WO2016118373A1
公开(公告)日：2016-07-28
申请号：PCT/US2016/013133
申请日：2016-01-13
Applicant: CISCO TECHNOLOGY, INC.
Inventor： DASGUPTA, Sukrit , VASSEUR, Jean-Philippe
IPC: H04L29/06
CPC classification number: H04L63/1425 , H04L63/1441 , H04L63/1458
Abstract: In one embodiment, a first device in a network identifies an anomalous traffic flow in the network. The first device reports the anomalous traffic flow to a supervisory device in the network. The first device determines a quarantine policy for the anomalous traffic flow. The first device determines an action policy for the anomalous traffic flow. The first device applies the quarantine and action policies to one or more packets of the anomalous traffic flow.
Abstract translation: 在一个实施例中，网络中的第一设备识别网络中的异常业务流。 第一个设备报告网络中监控设备的异常流量。 第一个设备确定异常流量的隔离策略。 第一个设备确定异常流量流的动作策略。 第一个设备将隔离和动作策略应用于异常流量的一个或多个数据包。
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公开(公告)号：WO2015175263A1
公开(公告)日：2015-11-19
申请号：PCT/US2015/029212
申请日：2015-05-05
Applicant: CISCO TECHNOLOGY, INC.
Inventor： VASSEUR, Jean-Philippe , DASGUPTA, Sukrit
IPC: H04L12/803 , H04L12/801
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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Patent Agency Ranking6.发明申请PREDICTIVE NETWORKING ARCHITECTURE FOR NEXT-GENERATION MULTISERVICE, MULTICARRIER WANS 审中-公开下一代多业务多媒体广域网预测网络架构
公开(公告)号：WO2015175260A1
公开(公告)日：2015-11-19
申请号：PCT/US2015/029152
申请日：2015-05-05
Applicant: CISCO TECHNOLOGY, INC.
Inventor： VASSEUR, Jean-Philippe , DASGUPTA, Sukrit , MERMOUD, Gregory
IPC: H04L12/24
CPC classification number: H04L41/16 , H04L41/0896 , H04L41/147 , H04L41/5003 , H04L41/5025 , H04L43/062 , H04L43/08 , H04L43/0876 , H04L47/22
Abstract: In one embodiment, network traffic data is received regarding traffic flowing through one or more routers in a network. A future traffic profile through the one or more routers is predicted by modeling the network traffic data. Network condition data for the network is received and future network performance is predicted by modeling the network condition data. A behavior of the network is adjusted based on the predicted future traffic profile and on the predicted network performance.
Abstract translation: 在一个实施例中，接收关于流经网络中的一个或多个路由器的流量的网络流量数据。 通过对网络流量数据建模来预测通过一个或多个路由器的未来流量简档。 接收网络的网络条件数据，并通过对网络条件数据建模来预测未来的网络性能。 基于预测的未来流量简档和预测的网络性能来调整网络的行为。
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7.发明申请LEARNING END-TO-END DELAYS IN COMPUTER NETWORKS FROM SPORADIC ROUND-TRIP DELAY PROBING 审中-公开从SPORADIC循环延迟探测计算机网络学习端到端延迟
公开(公告)号：WO2015103538A1
公开(公告)日：2015-07-09
申请号：PCT/US2015/010152
申请日：2015-01-05
Applicant: CISCO TECHNOLOGY, INC.
Inventor： MERMOUD, Grégory , VASSEUR, Jean-Philippe , DASGUPTA, Sukrit
CPC classification number: H04L43/0864 , G06N7/005 , G06N99/005 , H04L41/142 , H04L41/147 , H04L41/16 , H04L43/10 , H04L43/106 , H04W24/10
Abstract: Method to estimate transmission delays in network by using a learning machine. 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: 通过使用学习机估计网络传输延迟的方法。 在网络中执行周期性往返探测器，由此沿着从源到目的地并返回到源的特定通信路径来发送分组。 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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8.发明申请TRIGGERING REROUTES USING EARLY LEARNING MACHINE-BASED PREDICTION OF FAILURES 审中-公开使用早期学习机器进行故障预测的触发设备
公开(公告)号：WO2015103534A1
公开(公告)日：2015-07-09
申请号：PCT/US2015/010146
申请日：2015-01-05
Applicant: CISCO TECHNOLOGY, INC.
Inventor： VASSEUR, Jean-philippe , MERMOUD, Gregory , DASGUPTA, Sukrit
CPC classification number: 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
Abstract: Network metrics are collected and analyzed. It is predicted by using machine learning 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. Application to Low Power and Lossy Network (LLN).
Abstract translation: 收集和分析网络度量。 通过使用机器学习，可以基于收集和分析的网络度量来预测网络元件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故障。 应用于低功耗和有损网络（LLN）。
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9.发明申请DYNAMICALLY ADJUSTING A SET OF MONITORED NETWORK PROPERTIES USING DISTRIBUTED LEARNING MACHINE FEEDBACK 审中-公开使用分布式学习机反馈调整一组监控网络属性
公开(公告)号：WO2014123918A1
公开(公告)日：2014-08-14
申请号：PCT/US2014/014698
申请日：2014-02-04
Applicant: CISCO TECHNOLOGY, INC.
Inventor： VASSEUR, Jean-Philippe , MERMOUD, Grégory , DASGUPTA, Sukrit
CPC classification number: H04L43/02 , H04L41/16 , H04L43/103 , Y04S40/168
Abstract: In one embodiment, techniques are shown and described relating to dynamically adjusting a set of monitored network properties using distributed learning machine feedback. In particular, in one embodiment, a learning machine (or distributed learning machines) determines a plurality of monitored network properties in a computer network. From this, a subset of relevant network properties of the plurality of network properties may be determined, such that a corresponding subset of irrelevant network properties based on the subset of relevant network properties may also be determined. Accordingly, the computer network may be informed of the irrelevant network properties to reduce a rate of monitoring the irrelevant network properties.
Abstract translation: 在一个实施例中，示出和描述了关于使用分布式学习机器反馈动态地调整一组被监视的网络属性的技术。 特别地，在一个实施例中，学习机器（或分布式学习机器）在计算机网络中确定多个被监视的网络属性。 由此，可以确定多个网络属性的相关网络属性的子集，使得也可以确定基于相关网络属性子集的不相关网络属性的对应子集。 因此，可以向计算机网络通知不相关的网络属性，以降低监视不相关网络属性的速率。
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10.发明申请ACCELERATED LEARNING BY SHARING INFORMATION BETWEEN MULTIPLE LEARNING MACHINES 审中-公开通过多种学习机器之间的共享信息加速学习
公开(公告)号：WO2014123917A1
公开(公告)日：2014-08-14
申请号：PCT/US2014/014694
申请日：2014-02-04
Applicant: CISCO TECHNOLOGY, INC.
Inventor： MERMOUD, Gregory , VASSEUR, Jean-philippe , DASGUPTA, Sukrit
CPC classification number: G06N99/005 , H04L29/12 , H04L61/00
Abstract: 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.
Abstract translation: 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。
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