AI Cloud Migration Wave Planning for Automated Runbooks

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Solution Overview

Problem

Cloud migration is a costly and labor-intensive process due to the numerous factors that need to be considered during planning and execution, typically relying on human intervention.

Innovation Solution

A computer-implemented method and system utilizing artificial intelligence to generate a wave plan model, task portfolio, and runbooks for optimizing system migration to a cloud, leveraging passive and active learning capabilities to automate the migration process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If cloud migration is performed using traditional human planning and individual asset movement execution, then migration can be completed with existing processes, but the process becomes costly and labor-intensive

Engineering Contradiction:

Improvemigration process automationVSAvoidmigration planning complexity

Core Design Contradiction:

Ease of manufactureVSDevice complexity

Solution Approach 1:

The system enables self-service migration by automatically generating wave plans, task portfolios, and runbooks without human intervention. The processor autonomously analyzes assets, determines migration waves based on dependencies, and executes migration tasks, replacing manual human planning and execution processes

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes (human planning, asset assessment, migration execution) with an automated computational system that uses processors to generate wave plans, construct task portfolios, and execute migration runbooks, thereby reducing labor intensity and cost

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If multiple factors are considered in migration planning, then migration quality and reliability improve, but the planning process becomes more complex and time-consuming

Engineering Contradiction:

Improvemigration success rateVSAvoidplanning time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-generating wave plans that identify migration waves, pre-constructing task portfolios with all necessary tasks and dependencies, and pre-creating runbooks before actual migration execution. This preparation ensures all factors are considered upfront, improving reliability while reducing execution time

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the migration process into distinct waves and task portfolios, organizing complex migration factors into manageable segments. Each wave plan divides assets into migration waves based on dependencies, and task portfolios break down migration into discrete tasks, making complex planning tractable and executable

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12346725B2Artificial intelligence optimized cloud migration

Publication Date: 2025.07.01 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An approach to optimized migration of user assets to the cloud using artificial intelligence is presented. This approach may include user input and artificial intelligence trained with historical knowledge to generate rules. Migration models may be generated from the rules. A user may verify the migration models were successful. A task portfolio may be generated from the verified wave migration models. Runbook applications may be generated from the task portfolio and the migration may be executed using the runbooks.