METRIC-BASED ANOMALY DETECTION SYSTEM WITH EVOLVING MECHANISM IN LARGE-SCALE CLOUD

    公开(公告)号:US20210117259A1

    公开(公告)日:2021-04-22

    申请号:US17132690

    申请日:2020-12-23

    Abstract: A computer-implemented method is presented for detecting anomalies in dynamic datasets generated in a cloud computing environment. The method includes monitoring a plurality of cloud servers receiving a plurality of data points, employing a two-level clustering training module to generate micro-clusters from the plurality of data points, each of the micro-clusters representing a set of original data from the plurality of data points, employing a detecting module to detect normal data points, abnormal data points, and unknown data points from the plurality of data points via a detection model, employing an evolving module using a different evolving mechanism for each of the normal, abnormal, and unknown data points to evolve the detection model, and generating a system report displayed on a user interface, the system report summarizing the micro-cluster information.

    Performance anomaly detection
    55.
    发明授权

    公开(公告)号:US10977112B2

    公开(公告)日:2021-04-13

    申请号:US16253262

    申请日:2019-01-22

    Abstract: Embodiments facilitating performance anomaly detection are described. A computer-implemented method comprises: detecting, by a device operatively coupled to one or more processing units, based on monitoring data of a plurality of performance metrics of a monitored device, at least one trend within the monitoring data of the respective performance metrics; removing, by the device, the at least one trend from the monitoring data of the respective performance metrics to generate modified data of the respective performance metrics; and detecting, by the device, a performance anomaly based on the modified data of the respective performance metrics and a behavior clustering model comprising at least one steady state.

    Metric-based anomaly detection system with evolving mechanism in large-scale cloud

    公开(公告)号:US10949283B2

    公开(公告)日:2021-03-16

    申请号:US16181810

    申请日:2018-11-06

    Abstract: A computer-implemented method is presented for detecting anomalies in dynamic datasets generated in a cloud computing environment. The method includes monitoring a plurality of cloud servers receiving a plurality of data points, employing a two-level clustering training module to generate micro-clusters from the plurality of data points, each of the micro-clusters representing a set of original data from the plurality of data points, employing a detecting module to detect normal data points, abnormal data points, and unknown data points from the plurality of data points via a detection model, employing an evolving module using a different evolving mechanism for each of the normal, abnormal, and unknown data points to evolve the detection model, and generating a system report displayed on a user interface, the system report summarizing the micro-cluster information.

    Migrating virtual asset
    58.
    发明授权

    公开(公告)号:US10102027B2

    公开(公告)日:2018-10-16

    申请号:US15456762

    申请日:2017-03-13

    Abstract: Embodiments include methods and devices for migrating virtual assets over networks that have a first manager connected to a physical host a virtual machine run. Aspects include registering the physical host to a second manager in the network, creating the mapping relationship of the physical host between a database of the first manager and a database of the second manager and importing instance data and status data of the virtual machine of the physical host from the database of the first manager into the database of the second manager. Aspects also include switching the management for the physical host from the first manager to the second manager.

    Migrating virtual asset
    59.
    发明授权

    公开(公告)号:US10102026B2

    公开(公告)日:2018-10-16

    申请号:US15455258

    申请日:2017-03-10

    Abstract: Embodiments include methods and devices for migrating virtual assets over networks that have a first manager connected to a physical host a virtual machine run. Aspects include registering the physical host to a second manager in the network, creating the mapping relationship of the physical host between a database of the first manager and a database of the second manager and importing instance data and status data of the virtual machine of the physical host from the database of the first manager into the database of the second manager. Aspects also include switching the management for the physical host from the first manager to the second manager.

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