Patents by Inventor Koushik Nagaraj Godbole

Koushik Nagaraj Godbole 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).

  • Machine learning based resource availability prediction
    Patent number: 11514317
    Abstract: Requests from file system services of a storage system are registered. Each file system service, when executed, utilizes one or more resources of the storage system. Each request includes information describing resource requirements required by a respective file system service. Resource utilization data of the resources are collected over a period of time. The resource utilization data includes an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp. A machine learning model is trained to predict utilization patterns of the resources. Execution of the file system services are scheduled based on the predicted utilization patterns. Monitoring is conducted during the execution of the file system services. Based on the monitoring a determination is made as to whether the machine learning model should be retrained.
    Type: Grant
    Filed: March 25, 2020
    Date of Patent: November 29, 2022
    Assignee: EMC IP Holding Company LLC
    Inventors: Shashikanth Lakshmikantha, Sankalp Suhas Taralekar, Tuan Nguyen, Venkata Narasa Kumar Kuchi, Koushik Nagaraj Godbole
  • MACHINE LEARNING BASED RESOURCE AVAILABILITY PREDICTION
    Publication number: 20210303985
    Abstract: Requests from file system services of a storage system are registered. Each file system service, when executed, utilizes one or more resources of the storage system. Each request includes information describing resource requirements required by a respective file system service. Resource utilization data of the resources are collected over a period of time. The resource utilization data includes an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp. A machine learning model is trained to predict utilization patterns of the resources. Execution of the file system services are scheduled based on the predicted utilization patterns. Monitoring is conducted during the execution of the file system services. Based on the monitoring a determination is made as to whether the machine learning model should be retrained.
    Type: Application
    Filed: March 25, 2020
    Publication date: September 30, 2021
    Inventors: Shashikanth Lakshmikantha, Sankalp Suhas Taralekar, Tuan Nguyen, Venkata Narasa Kumar Kuchi, Koushik Nagaraj Godbole