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    Distributed machine learning autoscoring
    131.
    发明授权
    Distributed machine learning autoscoring 有权

    公开(公告)号：US09836696B2

    公开(公告)日：2017-12-05

    申请号：US14339347

    申请日：2014-07-23

    Applicant: Cisco Technology, Inc.

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

    IPC: G06F15/18 ,  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.

    CONSTRAINT-AWARE RESOURCE SYNCHRONIZATION ACROSS HYPER-DISTRIBUTED LEARNING SYSTEMS
    133.
    发明申请
    CONSTRAINT-AWARE RESOURCE SYNCHRONIZATION ACROSS HYPER-DISTRIBUTED LEARNING SYSTEMS 审中-公开

    公开(公告)号：US20170279849A1

    公开(公告)日：2017-09-28

    申请号：US15210974

    申请日：2016-07-15

    Applicant: Cisco Technology, Inc.

    Inventor： Yannick Weibel ,  Jean-Philippe Vasseur ,  Grégory Mermoud

    IPC: H04L29/06 ,  G06N5/04 ,  G06N99/00 ,  H04L29/08

    Abstract: In one embodiment, a device in a network receives data indicative of a target state for one or more distributed learning agents in the network. The device determines a difference between the target state and state information maintained by the device regarding the one or more distributed learning agents. The device calculates a synchronization penalty score for each of the one or more distributed learning agents. The device selects a particular one of the one or more distributed learning agents with which to synchronize, based on the synchronization penalty score for the selected distributed learning agent and on the determined difference between the target state and the state information regarding the selected distributed learning agent. The device initiates synchronization of the state information maintained by the device regarding the selected distributed learning agent with state information from the selected distributed learning agent.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    135.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 审中-公开

    公开(公告)号：US20170279828A1

    公开(公告)日：2017-09-28

    申请号：US15176652

    申请日：2016-06-08

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06

    CPC classification number: H04L63/1425 ,  H04L41/142 ,  H04L63/0236 ,  H04L63/1416 ,  H04L63/1458

    Abstract: In one embodiment, a device in a network maintains a plurality of anomaly detection models for different sets of aggregated traffic data regarding traffic in the network. The device determines a measure of confidence in a particular one of the anomaly detection models that evaluates a particular set of aggregated traffic data. The device dynamically replaces the particular anomaly detection model with a second anomaly detection model configured to evaluate the particular set of aggregated traffic data and has a different model capacity than that of the particular anomaly detection model. The device provides an anomaly event notification to a supervisory controller based on a combined output of the second anomaly detection model and of one or more of the anomaly detection models in the plurality of anomaly detection models.

    Distributed liveness reporting in a computer network
    139.
    发明授权
    Distributed liveness reporting in a computer network 有权

    公开(公告)号：US09705766B2

    公开(公告)日：2017-07-11

    申请号：US13924834

    申请日：2013-06-24

    Applicant: Cisco Technology, Inc.

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

    IPC: G06F15/16 ,  H04L12/26

    CPC classification number: H04L43/0811 ,  H04L43/0805 ,  Y04S40/168

    Abstract: In one embodiment, liveness reporting is performed using a distributed approach. The embodiments include a management node that is configured to receive a message containing an indication of activity or inactivity of one or more subject nodes, and determine which of the one or more subject nodes are active based on the received message. The indication is derived from one or more observer nodes observing network traffic of the one or more subject nodes. The embodiments further include one or more observer nodes configured to observe network traffic of the one or more subject nodes in the network, generate the message containing the indication of activity or inactivity of the one or more subject nodes, and transmit the message to the management node.

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