Patents by Inventor Athena Wong

Athena Wong 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: 10671445
    Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.
    Type: Grant
    Filed: December 4, 2017
    Date of Patent: June 2, 2020
    Assignee: CISCO TECHNOLOGY, INC.
    Inventors: Antonio Nucci, Dragan Milosavljevic, Ping Pamela Tang, Athena Wong, Alex V. Truong, Alexander Sasha Stojanovic, John Oberon, Prasad Potipireddi, Ahmed Khattab, Samudra Harapan Bekti
  • Publication number: 20190171494
    Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.
    Type: Application
    Filed: December 4, 2017
    Publication date: June 6, 2019
    Inventors: Antonio Nucci, Dragan Milosavljevic, Ping Pamela Tang, Athena Wong, Alex V. Truong, Alexander Sasha Stojanovic, John Oberon, Prasad Potipireddi, Ahmed Khattab, Samudra Harapan Bekti