Patents by Inventor Bing Pei

Bing Pei 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).

  • Two-tier capacity planning
    Patent number: 11995381
    Abstract: Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment.
    Type: Grant
    Filed: June 27, 2019
    Date of Patent: May 28, 2024
    Assignee: Splunk Inc.
    Inventors: Jie Cai, Yang Cao, Ning He, Bing Pei, Xiaolu Ye, Chong Yu, Aiping Zhang, Zhou Zhou
  • TWO-TIER CAPACITY PLANNING
    Publication number: 20200342068
    Abstract: Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment.
    Type: Application
    Filed: June 27, 2019
    Publication date: October 29, 2020
    Inventors: Jie Cai, Yang Cao, Ning He, Bing Pei, Xiaolu Ye, Chong Yu, Aiping Zhang, Zhou Zhou