FAST LEARNING TO TRAIN LEARNING MACHINES USING SHADOW JOINING
    151.
    发明申请
    FAST LEARNING TO TRAIN LEARNING MACHINES USING SHADOW JOINING 有权
    快速学习使用阴影加工训练学习机器

    公开(公告)号:US20140222725A1

    公开(公告)日:2014-08-07

    申请号:US13926526

    申请日:2013-06-25

    CPC classification number: G06N99/005

    Abstract: In one embodiment, a node receives a request to initiate a shadow joining operation to shadow join a field area router (FAR) of a computer network, and preserves its data structures and soft states. The shadow joining operation may then be initiated to shadow join the FAR, wherein shadow joining comprises preforming join operations without leaving a currently joined-FAR, and the node measures one or more joining metrics of the shadow joining operation, and reports them accordingly. In another embodiment, a FAR (or other management device) determines a set of nodes to participate in a shadow joining operation, and informs the set of nodes of the shadow joining operation to shadow join the FAR. The device (e.g., FAR) participates in the shadow joining operation, and receives reports of one or more joining metrics of the shadow joining operation measured by the set of nodes.

    Abstract translation: 在一个实施例中,节点接收发起影子加入操作以影响连接计算机网络的场区域路由器(FAR)的请求,并保留其数据结构和软状态。 然后可以启动阴影加入操作以影子连接FAR,其中阴影连接包括预先加入连接操作而不离开当前连接的FAR,并且节点测量阴影加入操作的一个或多个连接度量,并相应地报告。 在另一个实施例中,FAR(或其他管理设备)确定参与阴影加入操作的一组节点,并且通知该组节点的阴影加入操作以影响加入FAR。 设备(例如,FAR)参与阴影加入操作,并且接收由该组节点测量的阴影加入操作的一个或多个连接度量的报告。

    PROACTIVE AND SELECTIVE TIME-STAMPING OF PACKET HEADERS BASED ON QUALITY OF SERVICE EXPERIENCE AND NODE LOCATION
    152.
    发明申请
    PROACTIVE AND SELECTIVE TIME-STAMPING OF PACKET HEADERS BASED ON QUALITY OF SERVICE EXPERIENCE AND NODE LOCATION 有权
    基于服务质量和节点位置的分组头的主动和选择性时间戳

    公开(公告)号:US20140219133A1

    公开(公告)日:2014-08-07

    申请号:US13934929

    申请日:2013-07-03

    CPC classification number: H04L49/9057 H04L47/28

    Abstract: In one embodiment, a message is received at a node in a network indicating that the node is classified as a critical node, and requesting the node to proactively time-stamp data packets. Data packets are received from one or more child nodes of the node, and the node selects a data packet of the received data packets to time-stamp. Then, the node proactively inserts a time-stamp in the selected data packet. The time-stamped data packet is sent toward a central management node.

    Abstract translation: 在一个实施例中,在网络中的节点处接收到指示节点被分类为关键节点并且请求节点主动地对数据分组进行时间戳的消息。 从节点的一个或多个子节点接收数据分组,并且节点选择所接收的数据分组的数据分组进行时间戳。 然后,节点主动地在选择的数据分组中插入时间戳。 时间戳数据包被发送到中央管理节点。

    LLM-BASED NETWORK TROUBLESHOOTING USING EXPERT-CURATED RECIPES

    公开(公告)号:US20250150321A1

    公开(公告)日:2025-05-08

    申请号:US18386837

    申请日:2023-11-03

    Abstract: In one implementation, a device receives an input request for a large language model-based network troubleshooting agent regarding an issue in a network. The large language model-based network troubleshooting agent performs a lookup of a recipe based on the input request, wherein the recipe comprises contextual information for the issue. The device generates, by the large language model-based network troubleshooting agent, a prompt for a large language model based on the input request and on the recipe. The device provides, by the large language model-based network troubleshooting agent, the prompt to the large language model to troubleshoot the issue in the network.

    PROACTIVE BYPASS SELECTION BASED ON ROOT CAUSE ANALYSIS OF TRACEROUTES

    公开(公告)号:US20240406095A1

    公开(公告)日:2024-12-05

    申请号:US18204124

    申请日:2023-05-31

    Abstract: In one embodiment, a device identifies, based on traceroute information for a path in a network between an endpoint client and an online application, a particular segment of the path as most likely to cause degraded performance along the path. The device makes, using a prediction model, a prediction that routing traffic for the online application via the path will result in degraded quality of experience for the online application. The device obtains, based on the prediction, additional traceroute information in the network, to identify a bypass path in the network between the endpoint client and the online application that bypasses the particular segment. The device causes traffic for the online application to be routed along the bypass path.

    COMPUTING MOTIF SIGNATURES FOR QOE DISRUPTIONS
    157.
    发明公开

    公开(公告)号:US20240333624A1

    公开(公告)日:2024-10-03

    申请号:US18127907

    申请日:2023-03-29

    CPC classification number: H04L43/10

    Abstract: In one embodiment, a device obtains a set of probing motifs. Each probing motif groups similar patterns of path probing results for one or more path metrics in a network. The device generates signatures for the set of probing motifs. Each signature relates a probing strategy with a measure of performance of a classifier to detect that motif were path probing to be conducted in accordance with that probing strategy. The device selects, based on the signatures for the set of probing motifs, a particular probing strategy to use in the network. The device causes one or more probing agents in the network to conduct path probing in accordance with the particular probing strategy.

    COGNITIVE NETWORKS
    159.
    发明公开
    COGNITIVE NETWORKS 审中-公开

    公开(公告)号:US20240291723A1

    公开(公告)日:2024-08-29

    申请号:US18113722

    申请日:2023-02-24

    CPC classification number: H04L41/147 G06N7/01

    Abstract: In one embodiment, a device obtains cross-layer telemetry associated with an online application accessible via a network and from three or more layers of the network. The device estimates a quality of experience metric for the online application using the cross-layer telemetry as input to a cognitive model. The device selects a network action to increase the quality of experience metric estimated by the device. The device causes performance of the network action in the network.

    Application-specific high frequency passive probing

    公开(公告)号:US12069505B2

    公开(公告)日:2024-08-20

    申请号:US17853558

    申请日:2022-06-29

    Abstract: In one embodiment, a first networking device in a network coordinates, with a second networking device in the network, capture of packet maps for a traffic flow in the network associated with a particular application. The packet maps comprise multi-dimensional histograms indexed by identified properties of packets of the traffic flow and time. The first networking device inspects packets of the traffic flow, to identify properties of packets of the traffic flow. The first networking device generates a first packet map for the traffic flow based on the properties of the packets of the traffic flow identified by the first networking device. The first networking device causes a comparison between the first packet map and a second packet map generated by the second networking device to be used as a measure of application experience for the particular application.

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