Deriving highly interpretable cognitive patterns for network assurance

    公开(公告)号:US11049033B2

    公开(公告)日:2021-06-29

    申请号:US15869639

    申请日:2018-01-12

    Abstract: In one embodiment, a network assurance system that monitors a network labels time periods with positive labels, based on the network assurance system detecting problems in the network during the time periods. The network assurance system assigns tags to discrete portions of a feature space of measurements from the monitored network, based on whether a particular range of values in the feature space has a threshold probability of occurring during a positively-labeled time period. The network assurance system determines a set of the assigned tags that frequently co-occur with the positively-labeled time periods in which problems are detected in the network. The network assurance system causes performance of a mitigation action in the network based on the set of assigned tags that frequently co-occur with the positively-labeled time periods.

    ANALYZING THE IMPACT OF NETWORK EVENTS ACROSS TIME

    公开(公告)号:US20210067430A1

    公开(公告)日:2021-03-04

    申请号:US16560748

    申请日:2019-09-04

    Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization. The technology defines, based on a nature of the temporal event, a first period prior to the temporal event or a second period posterior to the temporal event. The technology compares network data collected in the first period and network data collected in the second period.

    DISTRIBUTED LEARNING MODEL FOR FOG COMPUTING

    公开(公告)号:US20200293925A1

    公开(公告)日:2020-09-17

    申请号:US16298465

    申请日:2019-03-11

    Abstract: The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

    Analyzing common traits in a network assurance system

    公开(公告)号:US10742486B2

    公开(公告)日:2020-08-11

    申请号:US15864565

    申请日:2018-01-08

    Abstract: In one embodiment, a network assurance system discretizes parameter values of a plurality of time series of measurements obtained from a monitored network by assigning tags to the parameter values. The network assurance system detects occurrences of a particular type of failure event in the monitored network. The network assurance system identifies a set of the assigned tags that frequently co-occur with the occurrences of the particular type of failure event. The network assurance system determines, using a Bayesian framework, rankings for the tags in the identified set based on how well each of the tags acts as a predictor of the failure event. The network assurance system initiates performance of a corrective measure for the failure event based in part on the determined rankings for the tags in the identified set.

    ANALYZING THE IMPACT OF NETWORK EVENTS ACROSS TIME

    公开(公告)号:US20230080544A1

    公开(公告)日:2023-03-16

    申请号:US18058103

    申请日:2022-11-22

    Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization; define a first period prior to the temporal event and a second period posterior to the temporal event; and compare network data collected in the first period and network data collected in the second period.

    Technologies for dynamically generating network topology-based and location-based insights

    公开(公告)号:US11296964B2

    公开(公告)日:2022-04-05

    申请号:US16563472

    申请日:2019-09-06

    Abstract: Technologies for dynamically generating topology and location based network insights are provided. In some examples, a method can include determining statistical changes in time series data including a series of data points associated with one or more conditions or parameters of a network; determining a period of time corresponding to one or more of the statistical changes in the time series data; obtaining telemetry data corresponding to a segment of the network and one or more time intervals, wherein a respective length of each time interval is based on a length of the period of time corresponding to the one or more of the statistical changes in the time series data; and generating, based on the telemetry data, insights about the segment of the network, the insights identifying a trend or statistical deviation in a behavior of the segment of the network during the one or more time intervals.

    Adaptive gossip protocol
    10.
    发明授权

    公开(公告)号:US11019143B2

    公开(公告)日:2021-05-25

    申请号:US16443627

    申请日:2019-06-17

    Abstract: Systems, methods, and computer-readable media for an adaptive gossip protocol. A node in a cluster can detect a gossip protocol synchronization triggering event which can include an indication that the node has received data from a second node via a gossip protocol, an update to data maintained by nodes in the cluster, and/or an operation. In response to the triggering event, the node can determine a dynamic gossip interval for disseminating data from the node to other nodes via a gossip protocol, the dynamic gossip interval being based on a synchronization state associated with the cluster and/or one or more gossip protocol events associated with the cluster. Next, the node can select a third node in the cluster for disseminating the data from the node to the third node. The node can then transmit the data to the third node via the gossip protocol based on the dynamic gossip interval.

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