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    Learning machine based computation of network join times
    45.
    发明授权
    Learning machine based computation of network join times 有权
    基于学习机的网络连接时间计算

    公开(公告)号：US09553773B2

    公开(公告)日：2017-01-24

    申请号：US13948367

    申请日：2013-07-23

    Applicant: Cisco Technology, Inc.

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

    IPC: G06F15/173 ,  H04L12/24

    CPC classification number: H04L41/16 ,  H04L41/142

    Abstract: In one embodiment, techniques are shown and described relating to learning machine based computation of network join times. In particular, in one embodiment, a device computes a join time of the device to join a computer network. During joining, the device sends a configuration request to a server, and receives instructions whether to provide the join time. The device may then provide the join time to a collector in response to instructions to provide the join time. In another embodiment, a collector receives a plurality of join times from a respective plurality of nodes having one or more associated node properties. The collector may then estimate a mapping between the join times and the node properties and determines a confidence interval of the mapping. Accordingly, the collector may then determine a rate at which nodes having particular node properties report their join times based on the confidence interval.

    Abstract translation: 在一个实施例中，与基于学习机的网络连接时间的计算相关的技术被示出和描述。 特别地，在一个实施例中，设备计算设备加入计算机网络的加入时间。 在加入过程中，设备向服务器发送配置请求，并接收指令是否提供加入时间。 响应于提供加入时间的指令，设备可以向收集器提供加入时间。 在另一个实施例中，收集器从具有一个或多个关联节点属性的相应多个节点接收多个连接时间。 然后，收集器可以估计连接时间和节点属性之间的映射，并确定映射的置信区间。 因此，收集器然后可以基于置信区间来确定具有特定节点属性的节点报告其连接时间的速率。

    Dynamically determining node locations to apply learning machine based network performance improvement
    46.
    发明授权
    Dynamically determining node locations to apply learning machine based network performance improvement 有权
    动态确定节点位置，以应用基于学习机的网络性能改进

    公开(公告)号：US09553772B2

    公开(公告)日：2017-01-24

    申请号：US13946227

    申请日：2013-07-19

    Applicant: Cisco Technology, Inc.

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

    IPC: G06F15/173 ,  H04L12/24 ,  H04L12/26

    CPC classification number: H04L41/16 ,  H04L41/0677 ,  H04L41/12 ,  H04L43/10

    Abstract: In one embodiment, techniques are shown and described relating to dynamically determining node locations to apply learning machine based network performance improvement. In particular, a degree of significance of nodes in a network, respectively, is calculated based on one or more significance factors. One or more significant nodes are then determined based on the calculated degree of significance. Additionally, a nodal region in the network of deteriorated network health is determined, and the nodal region of deteriorated network health is correlated with a significant node of the one or more significant nodes.

    Abstract translation: 在一个实施例中，显示和描述与动态确定节点位置以应用基于学习机的网络性能改进相关的技术。 特别地，基于一个或多个重要因素来分别计算网络中节点的重要程度。 然后基于所计算的显着程度来确定一个或多个有效节点。 另外，确定网络健康状况恶化的网络中的节点区域，将网络运行恶化的节点区域与一个或多个重要节点的重要节点相关联。

    DYNAMIC PATH SWITCHOVER DECISION OVERRIDE BASED ON FLOW CHARACTERISTICS
    48.
    发明申请
    DYNAMIC PATH SWITCHOVER DECISION OVERRIDE BASED ON FLOW CHARACTERISTICS 有权
    基于流动特性的动态路径切换决策

    公开(公告)号：US20160028616A1

    公开(公告)日：2016-01-28

    申请号：US14589421

    申请日：2015-01-05

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Sukrit Dasgupta

    IPC: H04L12/707 ,  H04L12/863 ,  H04L12/721 ,  H04L12/26 ,  H04L12/729

    CPC classification number: H04L45/22 ,  H04L41/147 ,  H04L43/0888 ,  H04L45/125 ,  H04L45/28 ,  H04L47/50

    Abstract: In one embodiment, a device in a network receives a switchover policy for a particular type of traffic in the network. The device determines a predicted effect of directing a traffic flow of the particular type of traffic from a first path in the network to a second path in the network. The device determines whether the predicted effect of directing the traffic flow to the second path would violate the switchover policy. The device causes the traffic flow to be routed via the second path in the network, based on a determination that the predicted effect of directing the traffic flow to the second path would not violate the switchover policy for the particular type of traffic.

    Abstract translation: 在一个实施例中，网络中的设备接收网络中特定类型的业务的切换策略。 设备确定将特定类型的业务的业务流从网络中的第一路径引导到网络中的第二路径的预测效果。 设备确定将流量指向第二路径的预测效果是否违反切换策略。 基于确定将业务流引导到第二路径的预测效果不会违反特定类型的业务的切换策略，该设备使业务流经由网络中的第二路径被路由。

    SCHEDULING PREDICTIVE MODELS FOR MACHINE LEARNING SYSTEMS
    49.
    发明申请
    SCHEDULING PREDICTIVE MODELS FOR MACHINE LEARNING SYSTEMS 有权
    调度机器学习系统的预测模型

    公开(公告)号：US20160028599A1

    公开(公告)日：2016-01-28

    申请号：US14591079

    申请日：2015-01-07

    Applicant: Cisco Technology, Inc.

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

    IPC: H04L12/26 ,  H04L12/24

    CPC classification number: H04L43/08 ,  H04L41/145 ,  H04L41/147 ,  H04L41/16

    Abstract: In one embodiment, a device in a network monitors performance data for a first predictive model. The first predictive model is used to make proactive decisions in the network. The device maintains a supervisory model based on the monitored performance data for the first predictive model. The device identifies a time period during which the supervisory model predicts that the first predictive model will perform poorly. The device causes a switchover from the first predictive model to a second predictive model at a point in time associated with the time period, in response to identifying the time period.

    Abstract translation: 在一个实施例中，网络中的设备监视用于第一预测模型的性能数据。 第一种预测模型用于在网络中进行主动决策。 该设备基于用于第一预测模型的监视的性能数据来维护监控模型。 设备识别监控模型预测第一预测模型将执行不良的时间段。 响应于识别时间间隔，该设备在与时间段相关联的时间点处导致从第一预测模型切换到第二预测模型。

    Distributed Machine Learning Autoscoring
    50.
    发明申请
    Distributed Machine Learning Autoscoring 有权
    分布式机器学习自动校准

    公开(公告)号：US20160026922A1

    公开(公告)日：2016-01-28

    申请号：US14339347

    申请日：2014-07-23

    Applicant: Cisco Technology, Inc.

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

    IPC: G06N5/04 ,  G06N99/00

    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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