AI Planner for Cloud Migration Pattern Recognition

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Solution Overview

Problem

Current cloud migration planning is often time-consuming, error-prone, and requires extensive manual effort, leading to suboptimal migration plans and increased resource utilization, with a lack of dynamic adaptation to new data points and operational knowledge.

Innovation Solution

A pattern-based artificial intelligence (AI) planner is employed to generate optimized migration plans by identifying reusable migration patterns, normalizing user input, and using machine learning to adjust plans in real-time, incorporating feedback and risk assessment to automate the migration process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual migration planning is used, then flexibility and adaptability to specific application needs are improved, but planning time and human effort increase significantly

Engineering Contradiction:

Improveadaptability to application needsVSAvoidplanning time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

An AI planner acts as an intermediary between manual planning processes and automated execution. The system ingests application configuration parameters, identifies migration patterns, and generates optimized migration plans, bridging the gap between flexible manual planning and efficient automated execution without requiring direct human involvement in plan creation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The migration planning system performs self-service by automatically analyzing application configurations, identifying appropriate migration patterns from a knowledge base, and generating migration plans without continuous human intervention. The system serves itself by maintaining and updating its own pattern knowledge base from executed migrations.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If manual migration planning is used, then complex decision-making can be performed, but error rates increase and reliability decreases

Engineering Contradiction:

Improvecomplex decision-making capabilityVSAvoidmigration plan accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system implements feedback mechanisms where migration patterns are learned from executed migrations and fed back into the knowledge base. This continuous learning process improves the accuracy and reliability of generated migration plans over time, reducing error rates while maintaining complex decision-making capabilities through pattern recognition.

Inventive Principle:

Principle #23Feedback

3Productivity

If traditional automation approaches are used, then execution speed is improved, but adaptability to new scenarios and learning from data decreases

Engineering Contradiction:

Improvemigration execution speedVSAvoidadaptability to new scenarios

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The migration planning system is dynamic and adaptive, using machine learning to continuously improve its pattern recognition capabilities. The system can adapt to new migration scenarios and learn from executed migrations, maintaining high execution speed while improving adaptability to new situations through ongoing pattern refinement.

Inventive Principle:

Principle #15Dynamics

4Reliability

If comprehensive migration planning is performed manually, then all aspects can be considered, but resource utilization increases and efficiency decreases

Engineering Contradiction:

Improvecomprehensiveness of migration planVSAvoidmigration efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system replaces manual mechanical planning processes with AI-based automated planning. The AI planner comprehensively analyzes application configurations and generates detailed migration plans that consider all necessary aspects, while significantly improving migration efficiency by eliminating manual planning bottlenecks and reducing resource utilization.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11061718B2Pattern-based artificial intelligence planner for computer environment migration

Publication Date: 2021.07.13 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An aspect of the invention includes a method for receiving, using a processor, a request to generate a migration plan for migrating an application from a source environment to a target environment. The request includes configuration parameters of the application. A set of possible actions that can be performed to migrate the application from the source environment to a target environment are identified, using the processor, based at least in part on the configuration parameters of the application. The migration plan is generated, using the processor, based at least in part on the request and the identified set of possible actions. The migration plan specifies a subset of the set of possible actions. The generating of the migration plan includes executing an artificial intelligence (AI) engine to identify patterns in the identified set of possible actions. The migration plan is output.