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

IPC: G06F9/44 ,  G06F8/65 ,  G06N20/00 ,  G06F8/60 ,  G06F16/906 ,  G06F8/61 ,  G06F9/50 ,  G06N5/02

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.

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

IPC: G06F8/65 ,  G06F8/60 ,  G06N20/00 ,  G06F16/906 ,  G06F8/61 ,  G06F9/50

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.

公开(公告)号：US12131142B2

公开(公告)日：2024-10-29

申请号：US17332649

申请日：2021-05-27

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

IPC: G06F8/65 ,  G06F8/60 ,  G06F8/61 ,  G06F9/50 ,  G06F16/906 ,  G06N5/022 ,  G06N20/00

CPC classification number: G06F8/65 ,  G06F8/60 ,  G06F8/61 ,  G06F9/5055 ,  G06F16/906 ,  G06N20/00 ,  G06N5/022

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.

公开(公告)号：US20210286611A1

公开(公告)日：2021-09-16

申请号：US17332649

申请日：2021-05-27

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

IPC: G06F8/65 ,  G06N20/00 ,  G06F8/60 ,  G06F16/906 ,  G06F8/61 ,  G06F9/50

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.

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

IPC: G06F8/65 ,  G06N20/00 ,  G06F8/60 ,  G06F16/906 ,  G06F8/61 ,  G06F9/50 ,  G06N5/02

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.

公开(公告)号：US20210132993A1

公开(公告)日：2021-05-06

申请号：US16670072

申请日：2019-10-31

Applicant: Oracle International Corporation

Inventor： Amol Achyut Chiplunkar ,  Prasad Ravuri ,  Karl Dias ,  Gayatri Tripathi ,  Shriram Krishnan ,  Chaitra Jayaram

IPC: G06F9/50 ,  G06F9/54 ,  G06F9/32

Abstract: The embodiments disclosed herein relate to predictive rate limiting. A workload for completing a request is predicted based on, for example, characteristics of a ruleset to be applied and characteristics of a target set upon which the ruleset is to be applied. The workload is mapped to a set of tokens or credits. If a requestor has sufficient tokens to cover the workload for the request, the request is processed. The request may be processed in accordance with a set of processing queues. Each processing queue is associated with a maximum per-tenant workload. A request may be added to a processing queue as long as adding the request does not result in exceeding the maximum per-tenant workload. Requests within a processing queue may be processed in a First In First Out (FIFO) order.

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

IPC: G06F8/60 ,  G06N99/00

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.

公开(公告)号：US11321135B2

公开(公告)日：2022-05-03

申请号：US16670072

申请日：2019-10-31

Applicant: Oracle International Corporation

Inventor： Amol Achyut Chiplunkar ,  Prasad Ravuri ,  Karl Dias ,  Gayatri Tripathi ,  Shriram Krishnan ,  Chaitra Jayaram

IPC: G06F9/46 ,  G06F9/50 ,  G06F9/54 ,  G06F9/32

Abstract: The embodiments disclosed herein relate to predictive rate limiting. A workload for completing a request is predicted based on, for example, characteristics of a ruleset to be applied and characteristics of a target set upon which the ruleset is to be applied. The workload is mapped to a set of tokens or credits. If a requestor has sufficient tokens to cover the workload for the request, the request is processed. The request may be processed in accordance with a set of processing queues. Each processing queue is associated with a maximum per-tenant workload. A request may be added to a processing queue as long as adding the request does not result in exceeding the maximum per-tenant workload. Requests within a processing queue may be processed in a First In First Out (FIFO) order.

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

IPC: G06F8/65 ,  G06F9/50 ,  G06F8/61 ,  G06F16/906 ,  G06F8/60 ,  G06N20/00

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.

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

IPC: G06F8/65 ,  G06N20/00 ,  G06F8/60 ,  G06F16/906 ,  G06F8/61 ,  G06F9/50 ,  G06N5/02

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.