Using unsupervised learning to monitor changes in fleet behavior

    公开(公告)号:US10324779B1

    公开(公告)日:2019-06-18

    申请号:US13924035

    申请日:2013-06-21

    Abstract: Embodiments are disclosed for determining whether a computing node is in a normal or an abnormal condition based on its characteristics relative to those of other computing nodes. In embodiments, log files for the computing node are used to develop a state model of the computing node, and where the state model differs between two similar computing nodes, an abnormality is identified. In other embodiments, characteristics about computing nodes (e.g., CPU resources used) are used to cluster those computing nodes, and those computing nodes that lie outside of a cluster are identified as abnormal.

    Determining abnormal conditions of host state from log files through Markov modeling

    公开(公告)号:US10255124B1

    公开(公告)日:2019-04-09

    申请号:US13924013

    申请日:2013-06-21

    Abstract: Embodiments are disclosed for determining whether a computing node is in a normal or an abnormal condition based on its characteristics relative to those of other computing nodes. In embodiments, log files for the computing node are used to develop a state model of the computing node, and where the state model differs between two similar computing nodes, an abnormality is identified. In other embodiments, characteristics about computing nodes (e.g., CPU resources used) are used to cluster those computing nodes, and those computing nodes that lie outside of a cluster are identified as abnormal.

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