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公开(公告)号：US20240403719A1
公开(公告)日：2024-12-05
申请号：US18732481
申请日：2024-06-03
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Sampanna 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：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：US20230075486A1
公开(公告)日：2023-03-09
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
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：US11138090B2
公开(公告)日：2021-10-05
申请号：US16168390
申请日：2018-10-23
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Sampanna Shahaji Salunke , Uri Shaft , Sumathi Gopalakrishnan
Abstract: Techniques for training and evaluating seasonal forecasting models are disclosed. In some embodiments, a network service generates, in memory, a set of data structures that separate sample values by season type and season space. The set of data structures may include a first set of clusters corresponding to different season types in the first season space and a second set of clusters corresponding to different season types in the second season space. The network service merges two or more clusters the first set and/or second set of clusters. Clusters from the first set are not merged with clusters from the second set. After merging the clusters, the network service determines a trend pattern for each of the remaining clusters in the first and second set of clusters. The network service then generates a forecast for a metric of a computing resource based on the trend patterns for each remaining cluster.
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公开(公告)号：US10915830B2
公开(公告)日：2021-02-09
申请号：US15643179
申请日：2017-07-06
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Sampanna Shahaji Salunke , Uri Shaft , Amit Ganesh , Sumathi Gopalakrishnan
Abstract: Techniques are described for generating predictive alerts. In one or more embodiments, a seasonal model is generated, the seasonal model representing one or more seasonal patterns within a first set of time-series data, the first set of time-series data comprising data points from a first range of time. A trend-based model is also generated to represent trending patterns within a second set of time-series data comprising data points from a second range of time that is different than the first range of time. A set of forecasted values is generated based on the seasonal model and the trend-based model. Responsive to determining that a set of alerting thresholds has been satisfied based on the set of forecasted values, an alert is generated.
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Patent Agency Ranking
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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公开(公告)号：US20180349797A1
公开(公告)日：2018-12-06
申请号：US15612999
申请日：2017-06-02
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Sampanna Shahaji Salunke , Uri Shaft , Amit Ganesh , Sumathi Gopalakrishnan
IPC: G06N99/00
Abstract: Techniques are described for applying what-f analytics to simulate performance of computing resources in cloud and other computing environments. In one or more embodiments, a plurality of time-series datasets are received including time-series datasets representing a plurality of demands on a resource and datasets representing performance metrics for a resource. Based on the datasets at least one demand propagation model and at least one resource prediction model are trained. Responsive to receiving an adjustment to a first set of one or more values associated with a first demand: (a) a second adjustment is generated for a second set of one or more values associated with a second demand; and (b) a third adjustment is generated for a third set of one or more values that is associated with the resource performance metric.
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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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