Patents by Inventor BHARGAVA RAM KALATHURU

BHARGAVA RAM KALATHURU 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).

  • Patent number: 10762086
    Abstract: Query execution status may be tracked to selectively route queries to resources for execution. The completion of queries executing at computing resources obtained from a pool of computing resources configured to execute queries may be detected. Instead of returning the computing resources to the pool, the computing resources may be identified as available in resource management data. When another query is received, the resource management data may be evaluated to select an available computing resource. The query may then be routed to the selected computing resource for execution.
    Type: Grant
    Filed: March 27, 2017
    Date of Patent: September 1, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Xing Wu, Bhargava Ram Kalathuru, Jian Fang, Yuanyuan Yue, Pratik Bhagwat Gawande, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Sumeetkumar Veniklal Maru, Armen Tangamyan, Yufeng Jiang
  • Publication number: 20200233869
    Abstract: The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.
    Type: Application
    Filed: April 3, 2020
    Publication date: July 23, 2020
    Applicant: Amazon Technologies, Inc.
    Inventors: Pratik Bhagwat Gawande, Sumeetkumar Veniklal Maru, Bhargava Ram Kalathuru, Jian Fang, Xing Wu, Yuanyuan Yue, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Armen Tangamyan, Yufeng Jiang, Marc Howard Beitchman, Andrew Edward Caldwell
  • Publication number: 20200204623
    Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.
    Type: Application
    Filed: February 28, 2020
    Publication date: June 25, 2020
    Applicant: Amazon Technologies, Inc.
    Inventors: Jonathan Daly Einkauf, Luca Natali, Bhargava Ram Kalathuru, Saurabh Dileep Baji, Abhishek Rajnikant Sinha
  • Patent number: 10614066
    Abstract: The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.
    Type: Grant
    Filed: March 27, 2017
    Date of Patent: April 7, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Pratik Bhagwat Gawande, Sumeetkumar Veniklal Maru, Bhargava Ram Kalathuru, Jian Fang, Xing Wu, Yuanyuan Yue, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Armen Tangamyan, Yufeng Jiang, Marc Howard Beitchman, Andrew Edward Caldwell
  • Patent number: 10581964
    Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.
    Type: Grant
    Filed: December 18, 2017
    Date of Patent: March 3, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Jonathan Daly Einkauf, Luca Natali, Bhargava Ram Kalathuru, Saurabh Dileep Baji, Abhishek Rajnikant Sinha
  • Publication number: 20180109610
    Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.
    Type: Application
    Filed: December 18, 2017
    Publication date: April 19, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: JONATHAN DALY EINKAUF, LUCA NATALI, BHARGAVA RAM KALATHURU, SAURABH DILEEP BAJI, ABHISHEK RAJNIKANT SINHA
  • Publication number: 20180060393
    Abstract: Queries may be received and executed by a managed query service. A query directed to data sets that are separately stored in a remote data store may be received. Computing resources to execute the query may be provisioned from a pool of computing resources that are configured to execute queries. The query may be routed to the provisioned computing resources to execute the query. Results may be obtained from the computing resource and provided to a submitter of the query.
    Type: Application
    Filed: March 27, 2017
    Publication date: March 1, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Bhargava Ram Kalathuru, Jian Fang, Xing Wu, Yuanyuan Yue, Pratik Bhagwat Gawande, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Sumeetkumar Veniklal Maru, Armen Tangamyan, Yufeng Jiang
  • Publication number: 20180060133
    Abstract: Event-driven management may be implemented for resource pools. Pool management events may be detected at computing resources in a resource pool. Operations based on the pool management events may then be performed at the computing resources. In some embodiments, pool management events may trigger operations to a recycle a computing resource for reuse in a resource pool or perform other resource lifecycle operations.
    Type: Application
    Filed: March 27, 2017
    Publication date: March 1, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Jian Fang, Xing Wu, Bhargava Ram Kalathuru, Yuanyuan Yue, Pratik Bhagwat Gawande, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Sumeetkumar Veniklal Maru, Armen Tangamyan, Yufeng Jiang
  • Publication number: 20180060400
    Abstract: Query execution status may be tracked to selectively route queries to resources for execution. The completion of queries executing at computing resources obtained from a pool of computing resources configured to execute queries may be detected. Instead of returning the computing resources to the pool, the computing resources may be identified as available in resource management data. When another query is received, the resource management data may be evaluated to select an available computing resource. The query may then be routed to the selected computing resource for execution.
    Type: Application
    Filed: March 27, 2017
    Publication date: March 1, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Xing Wu, Bhargava Ram Kalathuru, Jian Fang, Yuanyuan Yue, Pratik Bhagwat Gawande, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Sumeetkumar Veniklal Maru, Armen Tangamyan, Yufeng Jiang
  • Publication number: 20180060394
    Abstract: The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.
    Type: Application
    Filed: March 27, 2017
    Publication date: March 1, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Pratik Bhagwat Gawande, Sumeetkumar Veniklal Maru, Bhargava Ram Kalathuru, Jian Fang, Xing Wu, Yuanyuan Yue, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Armen Tangamyan, Yufeng Jiang, Marc Howard Beitchman, Andrew Edward Caldwell
  • Publication number: 20180060132
    Abstract: Stateful resource pool management may be implemented for executing jobs. Metrics for pools of computing resources that are configured to execute jobs on behalf of network-based services may be collected. The metrics may be evaluated to detect a modification event for a pool of computing resources. The pool of computing resources may then be modified according to the detected modification event for the pool. Evaluation of metrics may be performed automatically as part of monitoring a resource pool, in some embodiments.
    Type: Application
    Filed: March 27, 2017
    Publication date: March 1, 2018
    Applicant: Amazon Technologies, Inc.
    Inventors: Sumeetkumar Veniklal Maru, Bhargava Ram Kalathuru, Jian Fang, Xing Wu, Yuanyuan Yue, Pratik Bhagwat Gawande, Turkay Mert Hocanin, Jason Douglas Denton, Luca Natali, Rahul Sharma Pathak, Abhishek Rajnikant Sinha, Armen Tangamyan, Yufeng Jiang
  • Patent number: 9848041
    Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.
    Type: Grant
    Filed: May 1, 2015
    Date of Patent: December 19, 2017
    Assignee: Amazon Technologies, Inc.
    Inventors: Jonathan Daly Einkauf, Luca Natali, Bhargava Ram Kalathuru, Saurabh Dileep Baji, Abhishek Rajnikant Sinha
  • Publication number: 20160323377
    Abstract: A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.
    Type: Application
    Filed: May 1, 2015
    Publication date: November 3, 2016
    Applicant: AMAZON TECHNOLOGIES, INC.
    Inventors: JONATHAN DALY EINKAUF, LUCA NATALI, BHARGAVA RAM KALATHURU, SAURABH DILEEP BAJI, ABHISHEK RAJNIKANT SINHA