Cumulative node heartbeat relay agents in constrained computer networks
    71.
    发明授权
    Cumulative node heartbeat relay agents in constrained computer networks 有权
    受限计算机网络中的累积节点心跳中继代理

    公开(公告)号:US09178772B2

    公开(公告)日:2015-11-03

    申请号:US13926761

    申请日:2013-06-25

    CPC classification number: H04L41/145 H04L43/0805 H04L43/10 Y04S40/168

    Abstract: In one embodiment, a message instructing a particular node to act as a heartbeat relay agent is received at the particular node in a network. The particular node is selected to receive the message based on a centrality of the particular node. Heartbeat messages are then collected from child nodes of the particular node in the network. Based on the collected heartbeat messages, a heartbeat report is generated, and the report is transmitted to a collecting node in the network.

    Abstract translation: 在一个实施例中,指示特定节点充当心跳中继代理的消息在网络中的特定节点处被接收。 选择特定节点以基于特定节点的中心性接收消息。 然后从网络中的特定节点的子节点收集心跳消息。 基于收集到的心跳消息,生成心跳报告,并将报告发送到网络中的收集节点。

    MIXED DISTRIBUTED/CENTRALIZED ROUTING TECHNIQUES BASED ON CLOSED-LOOP FEEDBACK FROM A LEARNING MACHINE TO AVOID DARK ZONES
    72.
    发明申请
    MIXED DISTRIBUTED/CENTRALIZED ROUTING TECHNIQUES BASED ON CLOSED-LOOP FEEDBACK FROM A LEARNING MACHINE TO AVOID DARK ZONES 有权
    基于闭环反馈的混合分布式/集中式路由技术从学习机到避免分区

    公开(公告)号:US20150195126A1

    公开(公告)日:2015-07-09

    申请号:US14164469

    申请日:2014-01-27

    Abstract: In one embodiment, a routing topology of a network including nodes interconnected by communication links is determined, and activity in the network is monitored to determine a normal behavior of the communication links. Weak communication links in the network that deviate from the determined normal behavior are detected, and it is then determined whether the weak communication links are spatially correlated based on the determined topology of the network. In response to the weak communication links being spatially correlated, a region of the network affected by the weak communication links is identified as a dark zone that is to be avoided when routing data packets in the network.

    Abstract translation: 在一个实施例中,确定包括由通信链路互连的节点的网络的路由拓扑,并且监视网络中的活动以确定通信链路的正常行为。 检测到网络中偏离所确定的正常行为的弱通信链路,然后基于确定的网络拓扑确定弱通信链路是否在空间上相关。 响应于弱的通信链路在空间上相关,受弱通信链路影响的网络的区域被识别为当在网络中路由数据分组时要避免的暗区。

    LIGHTWEIGHT MULTICAST ACKNOWLEDGEMENT TECHNIQUE IN COMMUNICATION NETWORKS
    73.
    发明申请
    LIGHTWEIGHT MULTICAST ACKNOWLEDGEMENT TECHNIQUE IN COMMUNICATION NETWORKS 有权
    通信网络中的轻量级多播确认技术

    公开(公告)号:US20150092529A1

    公开(公告)日:2015-04-02

    申请号:US14040844

    申请日:2013-09-30

    Abstract: In one embodiment, a message is received at a caching node in a network including an indication of the message's urgency. The message is transmitted to child nodes of the caching node, and upon transmitting the message, a retransmission timer is initiated when the message is urgent, based on the indication of the message's urgency. Then, one or more acknowledgements of receipt of the transmitted message are received from one or more of the child nodes, respectively. Upon expiration of the retransmission timer, when it is determined that one or more of the child nodes did not receive the transmitted message based on the received acknowledgements, the message is retransmitted to the child nodes.

    Abstract translation: 在一个实施例中,在网络中的高速缓存节点处接收消息,包括消息的紧急性的指示。 该消息被发送到高速缓存节点的子节点,并且在发送消息时,基于消息的紧急性的指示,当消息紧急时,发起重传定时器。 然后,分别从一个或多个子节点接收一个或多个接收到发送的消息的确认。 在重传定时器到期时,当确定一个或多个子节点基于接收到的确认没有接收到所发送的消息时,该消息被重传到子节点。

    FAST LEARNING TO TRAIN LEARNING MACHINES USING SMART-TRIGGERED REBOOT
    74.
    发明申请
    FAST LEARNING TO TRAIN LEARNING MACHINES USING SMART-TRIGGERED REBOOT 有权
    快速学习使用SMART-TRIGGERED REBOOT训练学习机

    公开(公告)号:US20140223155A1

    公开(公告)日:2014-08-07

    申请号:US13926447

    申请日:2013-06-25

    CPC classification number: G06F9/4405

    Abstract: In one embodiment, a triggered reboot of a field area router (FAR) of a computer network is initiated, and gathered states of the FAR are saved. The nodes in the computer network are informed of the triggered reboot, and then feedback may be collected from the nodes in response to the triggered reboot. As such, it can be determined whether to complete the triggered reboot based on the feedback, and the FAR is rebooted in response to determining to complete the triggered reboot. In another embodiment, a node receives information about the initiated triggered reboot of the FAR, and determines whether it has critical traffic. If not, the node buffers non-critical traffic and indicates positive feedback in response to the triggered reboot, but if so, then the node continues to process the critical traffic and indicates negative feedback in response to the triggered reboot.

    Abstract translation: 在一个实施例中,启动计算机网络的场区域路由器(FAR)的触发重新启动,并且保存FAR的收集状态。 计算机网络中的节点被通知触发的重新引导,然后可以响应于触发的重新启动从节点收集反馈。 因此,可以基于反馈来确定是否完成触发的重新启动,并且响应于确定完成触发的重新启动而重新启动FAR。 在另一个实施例中,节点接收关于FAR的启动的触发重启的信息,并且确定它是否具有关键业务。 如果没有,节点将缓存非关键流量,并响应触发的重新启动来指示正反馈,但如果是,则节点继续处理关键流量,并响应于触发的重新启动来指示负反馈。

    LEARNING MACHINE BASED DETECTION OF ABNORMAL NETWORK PERFORMANCE
    75.
    发明申请
    LEARNING MACHINE BASED DETECTION OF ABNORMAL NETWORK PERFORMANCE 有权
    基于学习机的检测异常网络性能

    公开(公告)号:US20140222998A1

    公开(公告)日:2014-08-07

    申请号:US13955860

    申请日:2013-07-31

    CPC classification number: H04L43/10 H04L41/147 H04L41/16 H04L43/08 Y04S40/168

    Abstract: In one embodiment, techniques are shown and described relating to learning machine based detection of abnormal network performance. In particular, in one embodiment, a border router receives a set of network properties xi and network performance metrics Mi from a network management server (NMS), and then intercepts xi and Mi transmitted from nodes in a computer network of the border router. As such, the border router may then build a regression function F based on xi and Mi, and can detect one or more anomalies in the intercepted xi and Mi based on the regression function F. In another embodiment, the NMS, which instructed the border router, receives the detected anomalies from the border router.

    Abstract translation: 在一个实施例中,与基于学习机的异常网络性能检测有关的技术被显示和描述。 特别地,在一个实施例中,边界路由器从网络管理服务器(NMS)接收一组网络属性xi和网络性能度量Mi,然后截取从边界路由器的计算机网络中的节点发送的xi和Mi。 因此,边界路由器然后可以基于xi和Mi建立回归函数F,并且可以基于回归函数F来检测截取的xi和Mi中的一个或多个异常。在另一个实施例中,指示边界的NMS 路由器,从边界路由器接收检测到的异常。

    DISTRIBUTED ARCHITECTURE FOR MACHINE LEARNING BASED COMPUTATION USING A DECISION CONTROL POINT
    76.
    发明申请
    DISTRIBUTED ARCHITECTURE FOR MACHINE LEARNING BASED COMPUTATION USING A DECISION CONTROL POINT 有权
    使用决策控制点进行机器学习计算的分布式架构

    公开(公告)号:US20140222730A1

    公开(公告)日:2014-08-07

    申请号:US13954230

    申请日:2013-07-30

    CPC classification number: G06N99/005 G06F11/3433 H04L67/1029

    Abstract: In one embodiment, a request is received from a requesting node in a network to assist in distributing a task of the requesting node. Upon receiving the message, a capability to perform the task of one or more helping nodes in the network is evaluated, and a helping node of the one or more helping nodes is selected to perform the task based on the evaluated capability of the selected helping node. The distribution of the task is then authorized from the requesting node to the selected helping node.

    Abstract translation: 在一个实施例中,从网络中的请求节点接收到请求以帮助分发请求节点的任务。 在接收到消息时,评估执行网络中的一个或多个帮助节点的任务的能力,并且基于所选择的帮助节点的评估能力来选择一个或多个帮助节点的帮助节点来执行任务 。 然后从请求节点向所选择的帮助节点授权任务的分发。

    PRIVATE SD-WAN PEERING
    77.
    发明申请

    公开(公告)号:US20240388526A1

    公开(公告)日:2024-11-21

    申请号:US18199509

    申请日:2023-05-19

    Abstract: In one embodiment, a device identifies available resources of a tunnel in a first software defined network. The device provides, based on the available resources, an indication that the tunnel is available to convey traffic sent by a second software defined network. The device receives, based on the indication, a request to convey traffic sent by the second software defined network via the tunnel in the first software defined network. The device configures a peering node in the first software defined network to connect the second software defined network to the tunnel to allow the traffic sent by the second software defined network to be conveyed via the tunnel.

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