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公开(公告)号：US20190361693A1
公开(公告)日：2019-11-28
申请号：US16534896
申请日：2019-08-07
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Timothy Mark Frazier , Shriram Krishnan , Uri Shaft , Amit Ganesh , Prasad Ravuri , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan
Abstract: Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.
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公开(公告)号：US11023221B2
公开(公告)日：2021-06-01
申请号：US16854635
申请日：2020-04-21
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
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公开(公告)号：US20190102155A1
公开(公告)日：2019-04-04
申请号：US16042971
申请日：2018-07-23
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
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公开(公告)号：US10789065B2
公开(公告)日：2020-09-29
申请号：US15972650
申请日：2018-05-07
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Timothy Mark Frazier , Shriram Krishnan, Sr. , Uri Shaft , Prasad Ravuri , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan
IPC: G06F8/71 , G06F16/901 , G06F8/60 , G06F16/2457 , G06F16/28
Abstract: Techniques for analyzing, understanding, and remediating differences in configurations among many software resources are described herein. Machine learning processes are applied to determine a small feature set of parameters from among the complete set of parameters configured for each software resource. The feature set of parameters is selected to optimally cluster configuration instances for each of the software resources. Once clustered based on the values of the feature set of parameters, a graph is generated for each cluster of configuration instances that depicts the differences among the configuration instances within the cluster. An interactive visualization tool renders the graph in a user interface, and a management tool allows changes to the graph and changes to the configuration of one or more software resources.
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公开(公告)号：US20200125474A1
公开(公告)日：2020-04-23
申请号：US16166066
申请日：2018-10-20
Applicant: Oracle International Corporation
Inventor： Sampanna Shahaji Salunke , Dustin Garvey , Uri Shaft , Brent Arthur Enck , Timothy Mark Frazier , Sumathi Gopalakrishnan , Eric L. Sutton
IPC: G06F11/36
Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.
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Patent Agency Ranking
公开(公告)号：US10592230B2
公开(公告)日：2020-03-17
申请号：US16534896
申请日：2019-08-07
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Timothy Mark Frazier , Shriram Krishnan , Uri Shaft , Amit Ganesh , Prasad Ravuri , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan
Abstract: Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.
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公开(公告)号：US11675851B2
公开(公告)日：2023-06-13
申请号：US17479546
申请日：2021-09-20
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Brent Arthur Enck , Sampanna Shahaji Salunke , Uri Shaft , John Branson Bley , Timothy Mark Frazier , Sumathi Gopalakrishnan
IPC: G06F16/906 , G06F16/901 , G06F16/9038
CPC classification number: G06F16/906 , G06F16/9024 , G06F16/9038
Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.
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公开(公告)号：US20200249931A1
公开(公告)日：2020-08-06
申请号：US16854635
申请日：2020-04-21
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
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公开(公告)号：US10664264B2
公开(公告)日：2020-05-26
申请号：US16042971
申请日：2018-07-23
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
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公开(公告)号：US20190339965A1
公开(公告)日：2019-11-07
申请号：US15972650
申请日：2018-05-07
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Timothy Mark Frazier , Shriram Krishnan, SR. , Uri Shaft , Prasad Ravuri , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan
IPC: G06F8/71 , G06F16/901 , G06F16/28 , G06F16/2457 , G06F8/60
Abstract: Techniques for analyzing, understanding, and remediating differences in configurations among many software resources are described herein. Machine learning processes are applied to determine a small feature set of parameters from among the complete set of parameters configured for each software resource. The feature set of parameters is selected to optimally cluster configuration instances for each of the software resources. Once clustered based on the values of the feature set of parameters, a graph is generated for each cluster of configuration instances that depicts the differences among the configuration instances within the cluster. An interactive visualization tool renders the graph in a user interface, and a management tool allows changes to the graph and changes to the configuration of one or more software resources.
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