Patents by Inventor Alaa Shaabana

Alaa Shaabana 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).

  • Neural network model for predicting usage in a hyper-converged infrastructure
    Patent number: 12106203
    Abstract: Systems and methods for analyzing the usage of a set of workloads in a hyper-converged infrastructure are disclosed. A neural network model is trained based upon historical usage data of the set of workloads. The neural network model can make usage predictions of future demands on the set of workloads to minimize over-allocation or under-allocation of resources to the workloads.
    Type: Grant
    Filed: July 12, 2018
    Date of Patent: October 1, 2024
    Assignee: VMware LLC
    Inventors: Alaa Shaabana, Gregory Jean-Baptiste, Anant Agarwal, Rahul Chandrasekaran, Pawan Saxena
  • Machine learning system for workload failover in a converged infrastructure
    Patent number: 11379341
    Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
    Type: Grant
    Filed: April 7, 2021
    Date of Patent: July 5, 2022
    Assignee: VMware, Inc.
    Inventors: Aalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
  • MACHINE LEARNING SYSTEM FOR WORKLOAD FAILOVER IN A CONVERGED INFRASTRUCTURE
    Publication number: 20210255944
    Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
    Type: Application
    Filed: April 7, 2021
    Publication date: August 19, 2021
    Inventors: Aalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
  • Machine learning system for workload failover in a converged infrastructure
    Patent number: 10990501
    Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
    Type: Grant
    Filed: February 7, 2020
    Date of Patent: April 27, 2021
    Assignee: VMware, Inc.
    Inventors: Aalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
  • MACHINE LEARNING SYSTEM FOR WORKLOAD FAILOVER IN A CONVERGED INFRASTRUCTURE
    Publication number: 20200174904
    Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
    Type: Application
    Filed: February 7, 2020
    Publication date: June 4, 2020
    Inventors: Aalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
  • Machine learning system for workload failover in a converged infrastructure
    Patent number: 10585775
    Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
    Type: Grant
    Filed: July 24, 2018
    Date of Patent: March 10, 2020
    Assignee: VMware, Inc.
    Inventors: Aalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
  • MACHINE LEARNING SYSTEM FOR WORKLOAD FAILOVER IN A CONVERGED INFRASTRUCTURE
    Publication number: 20200034270
    Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
    Type: Application
    Filed: July 24, 2018
    Publication date: January 30, 2020
    Inventors: Aalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
  • NEURAL NETWORK MODEL FOR PREDICTING USAGE IN A HYPER-CONVERGED INFRASTRUCTURE
    Publication number: 20200019841
    Abstract: Systems and methods for analyzing the usage of a set of workloads in a hyper-converged infrastructure are disclosed. A neural network model is trained based upon historical usage data of the set of workloads. The neural network model can make usage predictions of future demands on the set of workloads to minimize over-allocation or under-allocation of resources to the workloads.
    Type: Application
    Filed: July 12, 2018
    Publication date: January 16, 2020
    Inventors: Alaa Shaabana, Gregory Jean-Baptiste, Anant Agarwal, Rahul Chandrasekaran, Pawan Saxena