Patents by Inventor Shriram Krishnan

Shriram Krishnan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210286611
    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.
    Type: Application
    Filed: May 27, 2021
    Publication date: September 16, 2021
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Patent number: 11023221
    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.
    Type: Grant
    Filed: April 21, 2020
    Date of Patent: June 1, 2021
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Publication number: 20210132993
    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.
    Type: Application
    Filed: October 31, 2019
    Publication date: May 6, 2021
    Applicant: Oracle International Corporation
    Inventors: Amol Achyut Chiplunkar, Prasad Ravuri, Karl Dias, Gayatri Tripathi, Shriram Krishnan, Chaitra Jayaram
  • Patent number: 10789065
    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.
    Type: Grant
    Filed: May 7, 2018
    Date of Patent: September 29, 2020
    Assignee: Oracle lnternational Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Timothy Mark Frazier, Shriram Krishnan, Sr., Uri Shaft, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20200249931
    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.
    Type: Application
    Filed: April 21, 2020
    Publication date: August 6, 2020
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Patent number: 10664264
    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.
    Type: Grant
    Filed: July 23, 2018
    Date of Patent: May 26, 2020
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Patent number: 10592230
    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.
    Type: Grant
    Filed: August 7, 2019
    Date of Patent: March 17, 2020
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Timothy Mark Frazier, Shriram Krishnan, Uri Shaft, Amit Ganesh, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Patent number: 10496396
    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.
    Type: Grant
    Filed: July 20, 2018
    Date of Patent: December 3, 2019
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Timothy Mark Frazier, Shriram Krishnan, Uri Shaft, Amit Ganesh, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20190361693
    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.
    Type: Application
    Filed: August 7, 2019
    Publication date: November 28, 2019
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Timothy Mark Frazier, Shriram Krishnan, Uri Shaft, Amit Ganesh, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20190339965
    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.
    Type: Application
    Filed: May 7, 2018
    Publication date: November 7, 2019
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Timothy Mark Frazier, Shriram Krishnan, SR., Uri Shaft, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20190138290
    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.
    Type: Application
    Filed: July 20, 2018
    Publication date: May 9, 2019
    Applicant: Oracle International Corporation
    Inventors: DUSTIN GARVEY, TIMOTHY MARK FRAZIER, SHRIRAM KRISHNAN, URI SHAFT, AMIT GANESH, PRASAD RAVURI, SAMPANNA SHAHAJI SALUNKE, SUMATHI GOPALAKRISHNAN
  • Publication number: 20190102155
    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.
    Type: Application
    Filed: July 23, 2018
    Publication date: April 4, 2019
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Patent number: 9137816
    Abstract: A common coexistence timer can be implemented on collocated or proximate wireless radio devices so that timing information associated with scheduled communications of each of the wireless radio devices is communicated with a common time reference. The coexistence timer provides the common time reference for the wireless radio devices. A transmitting wireless radio device can calculate a difference time associated with the timing information of a scheduled communication and can provide the difference time to a receiving wireless radio device. The receiving wireless radio device can convert the difference time into a time format corresponding to the receiving wireless radio device's local time reference. This can minimize interference between the collocated or proximate wireless radio devices, thus minimizing performance degradation, packet collision, and interference.
    Type: Grant
    Filed: November 2, 2012
    Date of Patent: September 15, 2015
    Assignee: QUALCOMM Incorporated
    Inventors: Olaf J Hirsch, Bharath Bhoopalam, Shriram Krishnan, Manev Luthra, Peter Van Horn
  • Patent number: 8335206
    Abstract: A common coexistence timer can be implemented on collocated or proximate wireless radio devices so that timing information associated with scheduled communications of each of the wireless radio devices is communicated with a common time reference. The coexistence timer provides the common time reference for the wireless radio devices. A transmitting wireless radio device can calculate a difference time associated with the timing information of a scheduled communication and can provide the difference time to a receiving wireless radio device. The receiving wireless radio device can convert the difference time into a time format corresponding to the receiving wireless radio device's local time reference. This can minimize interference between the collocated or proximate wireless radio devices, thus minimizing performance degradation, packet collision, and interference.
    Type: Grant
    Filed: March 4, 2010
    Date of Patent: December 18, 2012
    Assignee: Qualcomm Atheros, Inc.
    Inventors: Olaf Hirsch, Bharath Bhoopalam, Shriram Krishnan, Manev Luthra, Peter Van Horn
  • Patent number: 7657615
    Abstract: An approach for provisioning network devices generally involves supplying boot data to network devices over a network so that the network devices can be booted up in an imaging mode or an application mode, depending upon the particular boot data supplied to the network device. When booted up in the imaging mode, imaging data can be downloaded and stored on network devices. When booted up in the application mode, the network devices execute one or more programs contained in the image data stored on the network devices. The first and second boot data may be in the form of boot loader scripts. Furthermore, the first and second boot data may be provided to the network device in the payload of a dynamic host configuration protocol (DHCP) reply. The DHCP reply may be generated and provided by a DHCP server to the network device in response to receiving a DHCP request from the network device. The approach may be implemented using a secure network environment.
    Type: Grant
    Filed: December 8, 2003
    Date of Patent: February 2, 2010
    Assignee: Sun Microsystems, Inc.
    Inventors: Martin Patterson, Jayaraman Manni, Shriram Krishnan, Benjamin H. Stoltz, Christopher T. La
  • Patent number: 7237077
    Abstract: A method and apparatus for replicating an image from a source to a destination disk are provided. Specific embodiments may be optimized for single source to multiple destination replication requests, for example. In one embodiment, the present invention provides tools and techniques for synchronous data replication responsive to asynchronous same-source-to-different-destination replication requests.
    Type: Grant
    Filed: December 8, 2003
    Date of Patent: June 26, 2007
    Assignee: Sun Microsystems, Inc.
    Inventors: Martin Patterson, Shriram Krishnan, Jayaraman Manni, Benjamin H. Stoltz