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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
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.
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公开(公告)号：US12001926B2
公开(公告)日：2024-06-04
申请号：US16168377
申请日：2018-10-23
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Sampanna Shahaji Salunke , Uri Shaft , Sumathi Gopalakrishnan
Abstract: Techniques for machine-learning of long-term seasonal patterns are disclosed. In some embodiments, a network service receives a set of time-series data that tracks metric values of at least one computing resource over time. Responsive to receiving the time-series data, the network service detects a subset of metric values that are outliers and associated with a plurality of timestamps. The network service maps the plurality of timestamps to one or more encodings of at least one encoding space that defines a plurality of encodings for different seasonal patterns. Based on the mapped encodings, the network service generates a representation of a seasonal pattern. Based on the representation of the seasonal pattern, the network service may perform one or more operations in association with the at least one computing resource.
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公开(公告)号：US11949703B2
公开(公告)日：2024-04-02
申请号：US18055773
申请日：2022-11-15
Applicant: Oracle International Corporation
Inventor： Sampanna Shahaji Salunke , Dario Bahena Tapia , Dustin Garvey , Sumathi Gopalakrishnan , Neil Goodman
IPC: H04L29/06 , G06F18/2411 , G06N20/10 , H04L9/40
CPC classification number: H04L63/1425 , G06F18/2411 , G06N20/10
Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.
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公开(公告)号：US11537940B2
公开(公告)日：2022-12-27
申请号：US16410980
申请日：2019-05-13
Applicant: Oracle International Corporation
Inventor： Dario BahenaTapia , Sampanna Shahaji Salunke , Dustin Garvey , Sumathi Gopalakrishnan
Abstract: Systems and methods for unsupervised training and evaluation of anomaly detection models are described. In some embodiments, an unsupervised process comprises generating an approximation of a data distribution for a training dataset including varying values for a metric of a computing resource. The process further determines, based on the size of the training dataset, a first quantile probability and a second quantile probability that represent an interval for covering a prescribed proportion of values for the metric within a prescribed confidence level. The process further trains a lower limit of the anomaly detection model using a first quantile that represents the first quantile probability in the approximation of the data distribution and an upper limit using a second quantile that represents the second quantile probability in the approximation. The trained upper and lower limits may be used to monitor input data for anomalous behavior and, if detected, trigger responsive action(s).
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公开(公告)号：US11533326B2
公开(公告)日：2022-12-20
申请号：US16400392
申请日：2019-05-01
Applicant: Oracle International Corporation
Inventor： Sampanna Shahaji Salunke , Dario Bahena Tapia , Dustin Garvey , Sumathi Gopalakrishnan , Neil Goodman
Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.
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Patent Agency Ranking
公开(公告)号：US11288117B2
公开(公告)日：2022-03-29
申请号：US16532548
申请日：2019-08-06
Applicant: Oracle International Corporation
Inventor： Eric Sutton , Dustin Garvey , Sampanna Shahaji Salunke , Uri Shaft
Abstract: Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion. The system applies the remedial action to the particular problematic system behavior, despite already determining that the predicted effectiveness does not satisfy the one or more performance criteria.
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公开(公告)号：US11232133B2
公开(公告)日：2022-01-25
申请号：US16370227
申请日：2019-03-29
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Uri Shaft , Lik Wong , Amit Ganesh
IPC: G06F16/20 , G06F16/28 , G06F16/2458 , G06F17/18 , G06F21/55 , G06Q10/04 , G06Q30/02 , G06Q10/06 , G06N20/00
Abstract: Techniques are described for characterizing and summarizing seasonal patterns detected within a time series. According to an embodiment, a set of time series data is analyzed to identify a plurality of instances of a season, where each instance corresponds to a respective sub-period within the season. A first set of instances from the plurality of instances are associated with a particular class of seasonal pattern. After classifying the first set of instances, a second set of instances may remain unclassified or otherwise may not be associated with the particular class of seasonal pattern. Based on the first and second set of instances, a summary may be generated that identifies one or more stretches of time that are associated with the particular class of seasonal pattern. The one or more stretches of time may span at least one sub-period corresponding to at least one instance in the second set of instances.
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公开(公告)号：US20220004579A1
公开(公告)日：2022-01-06
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
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：US11126667B2
公开(公告)日：2021-09-21
申请号：US16383426
申请日：2019-04-12
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
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：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
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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