Patents by Inventor Robert MAESER

Robert MAESER 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).

  • System and method for analyzing cloud service provider trustworthiness and for predicting cloud service level agreement performance
    Patent number: 11615328
    Abstract: Effective management of cloud computing service levels (e.g. availability, performance, security) and financial risk is dependent on the cloud service provider's (CSP's) capability and trustworthiness. The invention applies industry cloud service level agreements (SLAs) with statistical and machine learning models for assessing CSPs and cloud services, and predicting performance and compliance against service levels. Cloud SLAs (ISO/IEC, EC, ENISA), cloud security requirements and compliance (CSA CCM, CAIQ), along with CSP performance (SLAs, cloud services) are analyzed via Graph Theory analysis and MCDA AHP to calculate CSP trustworthiness levels. CSP trustworthiness levels are input with CSP SLA content, cloud service performance measurements and configuration parameters into machine learning Regression analysis models to predict CSP cloud service performance and cloud SLA compliance, and enable model analysis and comparison.
    Type: Grant
    Filed: April 13, 2020
    Date of Patent: March 28, 2023
    Assignee: The George Washington University
    Inventor: Robert Maeser
  • SYSTEM AND METHOD FOR ANALYZING CLOUD SERVICE PROVIDER TRUSTWORTHINESS AND FOR PREDICTING CLOUD SERVICE LEVEL AGREEMENT PERFORMANCE
    Publication number: 20200327434
    Abstract: Effective management of cloud computing service levels (e.g. availability, performance, security) and financial risk is dependent on the cloud service provider's (CSP's) capability and trustworthiness. The invention applies industry cloud service level agreements (SLAs) with statistical and machine learning models for assessing CSPs and cloud services, and predicting performance and compliance against service levels. Cloud SLAs (ISO/IEC, EC, ENISA), cloud security requirements and compliance (CSA CCM, CAIQ), along with CSP performance (SLAs, cloud services) are analyzed via Graph Theory analysis and MCDA AHP to calculate CSP trustworthiness levels. CSP trustworthiness levels are input with CSP SLA content, cloud service performance measurements and configuration parameters into machine learning Regression analysis models to predict CSP cloud service performance and cloud SLA compliance, and enable model analysis and comparison.
    Type: Application
    Filed: April 13, 2020
    Publication date: October 15, 2020
    Inventor: Robert MAESER