Adaptive Prompt Orchestration for Legacy Cloud Migration
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Solution Overview
Problem
Cloud migrations are challenging due to the complexity of handling diverse components, understanding legacy platforms with lacking documentation, mapping dependencies between on-premises infrastructure and cloud resources, and addressing issues like data structure disparities and version incompatibilities, which can lead to inefficient resource allocation and data integrity problems.
Innovation Solution
A system utilizing generative artificial intelligence (AI) for adaptive prompt selection, inventory analysis, workload transformation, and validation mechanisms, including an adaptive prompt module, Rovr module, Movr module, and Audtr module, to streamline and accelerate cloud migration by ensuring compatibility, data consistency, and minimizing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cloud migration processes are used to handle diverse components and legacy platforms, then migration accuracy can be maintained through human expertise, but migration time and complexity increase significantly
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between legacy on-premises infrastructure and cloud targets. This AI system automatically analyzes code, identifies dependencies, maps data structures, and generates migration plans, replacing manual expert analysis while maintaining high accuracy. The intermediary processes include automated code parsing, dependency graph generation, and migration script creation, all performed by AI agents rather than human practitioners.
2Reliability
If comprehensive inventory analysis and dependency mapping are performed manually, then data integrity can be ensured, but the complexity and resource requirements of the migration process increase
Solution Approach 1:
The patent implements self-service capabilities where the AI system automatically performs inventory analysis, dependency mapping, and validation without requiring extensive human intervention. The AI agents autonomously scan on-premises infrastructure, parse codebases, identify relationships between components, and generate comprehensive migration plans. The system validates its own work through automated testing and verification, ensuring data integrity while reducing process complexity from the user perspective.
3Productivity
If automated code conversion tools are used to accelerate migration, then migration speed increases, but handling of diverse legacy platforms and version incompatibilities becomes more difficult
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting AI model parameters and conversion strategies based on the specific legacy platform being migrated from. The system identifies the source platform type (e.g., Teradata, Oracle, SAS, Mainframe), code version, and data structure characteristics, then adapts its conversion approach accordingly. This enables automated handling of diverse platforms while maintaining high migration speed, as the AI system learns and adjusts to different platform-specific patterns and requirements.
4Ease of manufacture
If detailed dependency mapping between on-premises infrastructure and cloud resources is performed, then resource allocation efficiency improves, but the time and computational resources required for analysis increase
Solution Approach 1:
The patent applies preliminary action by performing comprehensive dependency mapping and analysis before the actual migration execution. The AI system proactively scans the entire on-premises infrastructure, builds dependency graphs, identifies all relationships between applications, data, and infrastructure components, and pre-generates migration plans. This upfront analysis enables efficient resource allocation during migration execution, as all dependencies are already understood and mapped, eliminating the need for time-consuming analysis during the migration process itself.
Data Source
AI summary
A system and method for streamlining and accelerating cloud migration processes may include a plurality of modules including an adaptive prompt module configured to create a library of prompts, maintain the library of prompts, and select one or more prompts from at least one large language model (LLM) based on a prompt strategy that may consider which prompts in the library are most likely to elicit accurate and relevant responses from the at least one LLM. Another module may perform inventory analysis to enhance the prompt strategy. A third module may perform workload transformations based on the library of prompts, and a fourth module may provide validation mechanisms. The adaptive prompt module also may include a self-learning module configured to enable integration across the plurality of modules.