DYNAMIC DATA COLLECTION
    1.
    发明公开

    公开(公告)号:US20240241885A1

    公开(公告)日:2024-07-18

    申请号:US18155242

    申请日:2023-01-17

    CPC classification number: G06F16/2477 G06F16/24564

    Abstract: Disclosed embodiments provide techniques for dynamic data collection. The dynamic data collection includes determining a data generation temporal pattern. Based on the data generation temporal pattern, a data collection strategy is created. The data collection strategy can be based on one or more data collection goals. The data collection strategy can contain specific details on how data is to be collected. A data infrastructure evaluation is performed, which provides pricing models for resources such as electricity and/or network bandwidth. A data collection policy is created based on the data collection strategy and the data infrastructure evaluation. The data collection policy can contain specific details on when data is to be collected and what strategy to use for the collection. A data transfer schedule is created based on the data collection policy. The data transfer schedule determines when to collect data from one or more data source devices.

    Method and system for graph-based problem diagnosis and root cause analysis for IT operation

    公开(公告)号:US11636090B2

    公开(公告)日:2023-04-25

    申请号:US16819141

    申请日:2020-03-15

    Abstract: A computer-implemented method, system, and non-transitory machine readable medium for a graph-based analysis for an Information Technology (IT) operations includes generating a temporal graph by extracting one or more of operation objects, relations and attributes from operation data of workloads distributed across a plurality of levels of the IT operation within a predetermined time window. Anomalies are detected from the extracted operation data and annotating corresponding objects in the graph. A directional impact between corresponding objects on the temporal graph is determined, and the temporal graph is refined based on the determined directional impact. Accessible paths in the temporal graph indicating error propagation are searched, and potential causes for the detected anomalies in the temporal graph are identified. A list of the potential causes of the anomalies is generated, and a root cause ranked for each of the corresponding objects in the temporal graph.

    Determining characteristics of configuration files

    公开(公告)号:US11029969B2

    公开(公告)日:2021-06-08

    申请号:US16030949

    申请日:2018-07-10

    Abstract: Determining a characteristic of a configuration file that is used to discover configuration files in a target machine, a computer identifies, using information associated with a configuration item of a machine, a candidate configuration file related to the configuration item of the machine, from among a plurality of files from the machine. The computer extracts a value of a feature of the candidate configuration file and aggregates the candidate configuration file with a second candidate configuration file related to the same configuration item identified from among a plurality of files from a second machine, based on the extracted value. The computer then determines a configuration file related to the configuration item from among the aggregated candidate configuration files based on a result of the aggregation, and determines a characteristic of the configuration file related to the configuration item.

    PERFORMANCE ANOMALY DETECTION
    8.
    发明申请

    公开(公告)号:US20210117260A1

    公开(公告)日:2021-04-22

    申请号:US17136974

    申请日:2020-12-29

    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.

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