20240045831. UTILIZING A MACHINE LEARNING MODEL TO MIGRATE A SYSTEM TO A CLOUD COMPUTING ENVIRONMENT simplified abstract (Accenture Global Solutions Limited)

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UTILIZING A MACHINE LEARNING MODEL TO MIGRATE A SYSTEM TO A CLOUD COMPUTING ENVIRONMENT

Organization Name

Accenture Global Solutions Limited

Inventor(s)

Amar Ratanlal Bafna of Palghar (W) (IN)

Susan Patricia Mcnamara of Lamberhurst (GB)

Parag Rane of Thane West (IN)

Ankit Laxmichand Dedhia of Mumbai (IN)

Harsh Dhiraj Vira of Mumbai (IN)

Mayank Sudhir Singh of Mumbai (IN)

UTILIZING A MACHINE LEARNING MODEL TO MIGRATE A SYSTEM TO A CLOUD COMPUTING ENVIRONMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240045831 titled 'UTILIZING A MACHINE LEARNING MODEL TO MIGRATE A SYSTEM TO A CLOUD COMPUTING ENVIRONMENT

Simplified Explanation

The patent application describes a device that can receive logs and files from a system to be migrated to a cloud computing environment. The device can analyze the workload data, data lineage, and utilization patterns of the system to recommend a cloud architecture and determine the cost of migration. It can also use a q-matrix model to determine migration actions and perform those actions.

  • The device receives logs and files associated with a system to be migrated to the cloud.
  • It determines workload data associated with the system.
  • The device derives a data lineage for source data and target data.
  • It assesses the utilization pattern of the system.
  • The device processes the workload data, data lineage, and data identifying utilization with a model to label utilization features.
  • It recommends a cloud architecture based on the labeled utilization features.
  • The device uses a natural language processing model to determine the cost of migrating the system.
  • It processes the labeled utilization features, cloud architecture, and cost with a q-matrix model to determine migration actions.
  • The device performs actions based on the migration actions.

Potential Applications:

  • Cloud migration planning and optimization
  • Cost estimation for system migration to the cloud
  • Utilization analysis and recommendation for cloud architecture

Problems Solved:

  • Lack of automated tools for analyzing workload data and determining the best cloud architecture for system migration
  • Difficulty in estimating the cost of migrating a system to the cloud
  • Inefficient utilization of distributed computing features in the system

Benefits:

  • Streamlined and efficient migration process to the cloud
  • Cost-effective decision-making for system migration
  • Optimal utilization of distributed computing features in the cloud environment


Original Abstract Submitted

a device may receive logs and files associated with a system to be migrated to a cloud computing environment, and may determine workload data associated of the system. the device may derive a data lineage for source data and target data, and may assess a utilization pattern of the system. the device may process the workload data, the data lineage, and data identifying utilization of a distributed computing feature of the system, with a model, to label utilization features and to recommend a cloud architecture. the device may process the workload data, the data lineage, and the data identifying utilization, with a natural language processing model, to determine a cost of migrating the system. the device may process the labelled utilization features, the cloud architecture, and the cost, with a q-matrix model, to determine migration actions for migrating the system, and may perform actions based on the migration actions.

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