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    DISTRIBUTED MACHINE LEARNING AUTOSCORING
    1.
    发明申请
    DISTRIBUTED MACHINE LEARNING AUTOSCORING 审中-公开
    分布式机器学习自动化

    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

    IPC: G06N99/00 ,  H04L12/70

    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。

    LEARNING END-TO-END DELAYS IN COMPUTER NETWORKS FROM SPORADIC ROUND-TRIP DELAY PROBING
    2.
    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

    IPC: H04L12/24 ,  H04L12/26

    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。

    DYNAMICALLY ADJUSTING A SET OF MONITORED NETWORK PROPERTIES USING DISTRIBUTED LEARNING MACHINE FEEDBACK
    3.
    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

    IPC: H04L12/24 ,  H04L12/26

    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。

    PREDICTIVE LEARNING MACHINE-BASED APPROACH TO DETECT TRAFFIC OUTSIDE OF SERVICE LEVEL AGREEMENTS
    4.
    发明申请
    PREDICTIVE LEARNING MACHINE-BASED APPROACH TO DETECT TRAFFIC OUTSIDE OF SERVICE LEVEL AGREEMENTS 审中-公开
    检测服务水平协议之外的交通活动的基于预测学习机器的方法

    公开(公告)号：WO2015103523A1

    公开(公告)日：2015-07-09

    申请号：PCT/US2015/010119

    申请日：2015-01-05

    Applicant: CISCO TECHNOLOGY, INC.

    Inventor： VASSEUR, Jean-Philippe ,  MERMOUD, Grégory ,  DASGUPTA, Sukrit

    IPC: H04L12/24 ,  G06Q10/04

    CPC classification number: H04L41/5009 ,  G06N7/005 ,  G06N99/005 ,  G06Q10/04 ,  H04L41/12 ,  H04L41/147 ,  H04L41/16 ,  H04L41/5019 ,  H04L47/2425

    Abstract: A request to make a prediction regarding service level agreements (SLAs) in a Low Power and Lossy Network (LLN) is received. A network traffic parameter and an SLA requirement associated with the network traffic parameter according to the SLAs are also determined. A performance metric associated with traffic in the network that corresponds to the determined network traffic parameter is estimated. It may then be predicted whether the SLA requirement would be satisfied based on the estimated performance metric. A Learning Machine is used for predicting and routing topology in the network is dynamically adjusted in order to meet SLA requirements.

    Abstract translation: 接收到对低功耗和有损网络（LLN）中的服务级别协议（SLA）进行预测的请求。 还确定了与根据SLA的网络流量参数相关联的网络流量参数和SLA要求。 估计与确定的网络流量参数对应的网络中的流量相关联的性能度量。 然后可以基于估计的性能度量来预测是否满足SLA要求。 学习机用于网络中的预测和路由拓扑动态调整，以满足SLA要求。

    A MIXED CENTRALIZED/DISTRIBUTED ALGORITHM FOR RISK MITIGATION IN SPARESELY CONNECTED NETWORKS
    6.
    发明申请
    A MIXED CENTRALIZED/DISTRIBUTED ALGORITHM FOR RISK MITIGATION IN SPARESELY CONNECTED NETWORKS 审中-公开
    用于微小连接网络风险缓解的混合集中/分布式算法

    公开(公告)号：WO2014123924A1

    公开(公告)日：2014-08-14

    申请号：PCT/US2014/014709

    申请日：2014-02-04

    Applicant: CISCO TECHNOLOGY, INC.

    Inventor： VASSEUR, Jean-Philippe ,  MERMOUD, Grégory ,  DASGUPTA, Sukrit

    IPC: H04L12/729 ,  H04L12/721 ,  H04L12/803

    CPC classification number: H04L47/122 ,  H04L45/125 ,  H04L45/127

    Abstract: In one embodiment, techniques are shown and described relating to a mixed centralized/distributed algorithm for risk mitigation in sparsely connected networks. In particular, in one embodiment, a management node determines one or more weak point nodes in a shared-media communication network, where a weak point node is a node traversed by a relatively high amount of traffic as compared to other nodes in the network. In response to determining that a portion of the traffic can be routed over an alternate acceptable node, the management node instructs the portion of traffic to reroute over the alternate acceptable node.

    Abstract translation: 在一个实施例中，显示和描述与稀疏连接网络中的风险缓解的混合集中/分布式算法有关的技术。 特别地，在一个实施例中，管理节点确定共享 - 媒体通信网络中的一个或多个弱点节点，其中弱点节点是与网络中的其他节点相比较的由相对较高数量的业务量穿过的节点。 响应于确定业务的一部分可以在备用可接受节点上路由，管理节点指示业务部分重新路由替代可接受节点。

    LEARNING MACHINE BASED DETECTION OF ABNORMAL NETWORK PERFORMANCE
    7.
    发明申请
    LEARNING MACHINE BASED DETECTION OF ABNORMAL NETWORK PERFORMANCE 审中-公开
    基于学习机的检测异常网络性能

    公开(公告)号：WO2014123923A1

    公开(公告)日：2014-08-14

    申请号：PCT/US2014/014706

    申请日：2014-02-04

    Applicant: CISCO TECHNOLOGY, INC.

    Inventor： VASSEUR, Jean-Philippe ,  MERMOUD, Grégory ,  DASGUPTA, Sukrit

    IPC: H04L12/24 ,  H04L12/26

    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 x; and network performance metrics M; from a network management server (NMS), and then intercepts x; and M; 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 x; and Mi, and can detect one or more anomalies in the intercepted x; and M; 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相关的技术被示出和描述。 特别地，在一个实施例中，边界路由器接收一组网络属性x; 和网络性能指标M; 从网络管理服务器（NMS），然后拦截x; 和M; 从边界路由器的计算机网络中的节点发送。 因此，边界路由器然后可以基于x建立回归函数F; 和Mi，并且可以检测到被截获的x中的一个或多个异常; 和M; 基于回归函数F.在另一个实施例中，指示边界路由器的NMS从边界路由器接收检测到的异常。

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