Sumathi Gopalakrishnan

US Patent:

20100192136, Jul 29, 2010

Filed:

Jan 23, 2009

Appl. No.:

12/358439

Inventors:

Sumathi Gopalakrishnan - Fremont CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

G06F 9/45

US Classification:

717144

Abstract:

Systems and methods are provided that service a JavaServer Page (“JSP”), including receiving a request for a JSP page, parsing source code for the JSP page, creating a tree of the parsed source code. executing the tree in memory, and returning the requested JSP page. Accordingly, JSP pages do not require repeated recompilation, and JSP pages with customized content may be quickly regenerated with a low performance overhead.

US Patent:

20230075486, Mar 9, 2023

Filed:

Nov 15, 2022

Appl. No.:

18/055773

Inventors:

- Redwood Shores CA, US
Dario Bahena Tapia - Tlaquepaque, MX
Dustin Garvey - Exeter NH, US
Sumathi Gopalakrishnan - Fremont CA, US
Neil Goodman - San Rafael CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

H04L 9/40
G06N 20/10
G06K 9/62

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.

US Patent:

20210286611, Sep 16, 2021

Filed:

May 27, 2021

Appl. No.:

17/332649

Inventors:

- Redwood Shores CA, US
Amit Ganesh - San Jose CA, US
Uri Shaft - Fremont CA, US
Prasad Ravuri - San Jose CA, US
Long Yang - Redwood City CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Sumathi Gopalakrishnan - Fremont CA, US
Timothy Mark Frazier - Livermore CA, US
Shriram Krishnan - Oakland CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

G06F 8/65
G06N 20/00
G06F 8/60
G06F 16/906
G06F 8/61
G06F 9/50

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.

US Patent:

20200379882, Dec 3, 2020

Filed:

Aug 17, 2020

Appl. No.:

16/995282

Inventors:

- Redwood Shores CA, US
Dustin Garvey - Exeter NH, US
Uri Shaft - Fremont CA, US
Brent Arthur Enck - Roseville CA, US
Timothy Mark Frazier - Livermore CA, US
Sumathi Gopalakrishnan - Fremont CA, US
Eric L. Sutton - Redwood City CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

G06F 11/36

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.

US Patent:

20200364607, Nov 19, 2020

Filed:

May 13, 2019

Appl. No.:

16/410980

Inventors:

- Redwood Shores CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Dustin Garvey - Exeter NH, US
Sumathi Gopalakrishnan - Fremont CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

G06N 20/00
G06F 17/18
G06F 11/34
G06F 11/32
G06F 9/54

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).

US Patent:

20200351283, Nov 5, 2020

Filed:

May 1, 2019

Appl. No.:

16/400392

Inventors:

- Redwood Shores CA, US
Dario Bahena Tapia - Tlaquepaque, MX
Dustin Garvey - Exeter NH, US
Sumathi Gopalakrishnan - Fremont CA, US
Neil Goodman - San Rafael CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

H04L 29/06
G06K 9/62
G06N 20/10

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.

US Patent:

20200267057, Aug 20, 2020

Filed:

Feb 15, 2019

Appl. No.:

16/277012

Inventors:

- Redwood Shores CA, US
Neil Goodman - San Rafael CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Brent Arthur Enck - Roseville CA, US
Sumathi Gopalakrishnan - Fremont CA, US
Amit Ganesh - San Jose CA, US
Timothy Mark Frazier - Livermore CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

H04L 12/24
H04L 12/26

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.

US Patent:

20200249931, Aug 6, 2020

Filed:

Apr 21, 2020

Appl. No.:

16/854635

Inventors:

- Redwood Shores CA, US
Amit Ganesh - San Jose CA, US
Uri Shaft - Fremont CA, US
Prasad Ravuri - San Jose CA, US
Long Yang - Redwood City CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Sumathi Gopalakrishnan - Fremont CA, US
Timothy Mark Frazier - Livermore CA, US
Shriram Krishnan - Oakland CA, US

Assignee:

Oracle International Corporation - Redwood Shores CA

International Classification:

G06F 8/65
G06F 9/50
G06F 8/61
G06F 16/906
G06F 8/60
G06N 20/00

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.