Patents by Inventor Clifton Brent Walker

Clifton Brent Walker 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: 11868890
    Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b
    Type: Grant
    Filed: April 6, 2022
    Date of Patent: January 9, 2024
    Assignees: LANDMARK GRAPHICS CORPORATION, EMC IP HOLDING COMPANY LLC
    Inventors: Chandra Yeleshwarapu, Jonas F. Dias, Angelo Ciarlini, Romulo D. Pinho, Vinicius Gottin, Andre Maximo, Edward Pacheco, David Holmes, Keshava Rangarajan, Scott David Senften, Joseph Blake Winston, Xi Wang, Clifton Brent Walker, Ashwani Dev, Nagaraj Sirinivasan
  • Publication number: 20220300812
    Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b
    Type: Application
    Filed: April 6, 2022
    Publication date: September 22, 2022
    Applicants: Landmark Graphics Corporation, EMC IP Holding Company LLC
    Inventors: Chandra YELESHWARAPU, Jonas F. DIAS, Angelo CIARLINI, Romulo D. Pinho, Vinicius GOTTIN, Andre MAXIMO, Edward PACHECO, David HOLMES, Keshava RANGARAJAN, Scott David SENFTEN, Joseph Blake WINSTON, Xi WANG, Clifton Brent WALKER, Ashwani DEV, Nagaraj SIRINIVASAN
  • Patent number: 11315014
    Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b
    Type: Grant
    Filed: August 16, 2018
    Date of Patent: April 26, 2022
    Assignee: EMC IP HOLDING COMPANY LLC
    Inventors: Jonas F. Dias, Angelo Ciarlini, Romulo D. Pinho, Vinicius Gottin, Andre Maximo, Edward Pacheco, David Holmes, Keshava Rangarajan, Scott David Senften, Joseph Blake Winston, Xi Wang, Clifton Brent Walker, Ashwani Dev, Chandra Yeleshwarapu, Nagaraj Srinivasan
  • Publication number: 20200057675
    Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b
    Type: Application
    Filed: August 16, 2018
    Publication date: February 20, 2020
    Inventors: Jonas F. Dias, Angelo Ciarlini, Romulo D. Pinho, Vinicius Gottin, Andre Maximo, Edward Pacheco, David Holmes, Keshava Rangarajan, Scott David Senften, Joseph Blake Winston, Xi Wang, Clifton Brent Walker, Ashwani Dev, Chandra Yeleshwarapu, Nagaraj Srinivasan