Patents by Inventor Murali Krishna Redrowthu

Murali Krishna Redrowthu 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).

  • Selecting threads for concurrent processing of data
    Patent number: 11593163
    Abstract: The embodiments disclosed herein relate to using machine learning to allocate a number of concurrent processes for minimizing the completion time for executing a task having multiple subtasks. Historical data comprising a variety of subtask types with actual completion times is mined to create a set of statistical models for predicting completion time for a type of subtask. To minimize the total time to complete execution of a new task, a certain number of threads is allocated to execute subtasks of the new task. The certain number of threads is determined based on the predicted completion time for the subtasks using the respective statistical model. Threads are assigned to subtasks based on the predicted completion time for the subtasks, and the subtasks assigned to each thread are scheduled for execution.
    Type: Grant
    Filed: November 13, 2020
    Date of Patent: February 28, 2023
    Assignee: Oracle International Corporation
    Inventors: Subramanian Chittoor Venkataraman, Balender Kumar, Sai Krishna Sujith Alamuri, Murali Krishna Redrowthu, Srividya Bhavani Sivaraman
  • SELECTING THREADS FOR CONCURRENT PROCESSING OF DATA
    Publication number: 20210064424
    Abstract: The embodiments disclosed herein relate to using machine learning to allocate a number of concurrent processes for minimizing the completion time for executing a task having multiple subtasks. Historical data comprising a variety of subtask types with actual completion times is mined to create a set of statistical models for predicting completion time for a type of subtask. To minimize the total time to complete execution of a new task, a certain number of threads is allocated to execute subtasks of the new task. The certain number of threads is determined based on the predicted completion time for the subtasks using the respective statistical model. Threads are assigned to subtasks based on the predicted completion time for the subtasks, and the subtasks assigned to each thread are scheduled for execution.
    Type: Application
    Filed: November 13, 2020
    Publication date: March 4, 2021
    Applicant: Oracle International Corporation
    Inventors: Subramanian Chittoor Venkataraman, Balender Kumar, Sai Krishna Sujith Alamuri, Murali Krishna Redrowthu, Srividya Bhavani Sivaraman
  • Selecting threads for concurrent processing of data
    Patent number: 10871989
    Abstract: The embodiments disclosed herein relate to using machine learning to allocate a number of concurrent processes for minimizing the completion time for executing a task having multiple subtasks. Historical data comprising a variety of subtask types with actual completion times is mined to create a set of statistical models for predicting completion time for a type of subtask. To minimize the total time to complete execution of a new task, a certain number of threads is allocated to execute subtasks of the new task. The certain number of threads is determined based on the predicted completion time for the subtasks using the respective statistical model. Threads are assigned to subtasks based on the predicted completion time for the subtasks, and the subtasks assigned to each thread are scheduled for execution.
    Type: Grant
    Filed: October 18, 2018
    Date of Patent: December 22, 2020
    Assignee: Oracle International Corporation
    Inventors: Subramanian Chittoor Venkataraman, Balender Kumar, Sai Krishna Sujith Alamuri, Murali Krishna Redrowthu, Srividya Bhavani Sivaraman
  • SELECTING THREADS FOR CONCURRENT PROCESSING OF DATA
    Publication number: 20200125400
    Abstract: The embodiments disclosed herein relate to using machine learning to allocate a number of concurrent processes for minimizing the completion time for executing a task having multiple subtasks. Historical data comprising a variety of subtask types with actual completion times is mined to create a set of statistical models for predicting completion time for a type of subtask. To minimize the total time to complete execution of a new task, a certain number of threads is allocated to execute subtasks of the new task. The certain number of threads is determined based on the predicted completion time for the subtasks using the respective statistical model. Threads are assigned to subtasks based on the predicted completion time for the subtasks, and the subtasks assigned to each thread are scheduled for execution.
    Type: Application
    Filed: October 18, 2018
    Publication date: April 23, 2020
    Applicant: Oracle International Corporation
    Inventors: Subramanian Chittoor Venkataraman, Balender Kumar, Sai Krishna Sujith Alamuri, Murali Krishna Redrowthu, Srividya Bhavani Sivaraman