Latent Computing Property Preference Discovery for Migration Plans
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Solution Overview
Problem
Existing computing environment migration processes face challenges in understanding latent client preferences and recommending optimal migration plans, as they rely heavily on human expertise and are inefficient in processing large data sources, leading to suboptimal adjustments in migrating applications to new computing environments.
Innovation Solution
A system employing a model to discover latent computing property preferences of entities in a first computing environment and recommend migration plans to a second environment, utilizing artificial intelligence and machine learning to analyze patterns and feedback data for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI/ML models are used to discover latent preferences and recommend migration plans, then productivity and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent introduces an automated analysis component as an intermediary between the entity's computing environment and the migration planning process. This component employs AI/ML models to automatically discover latent preferences and generate recommendations, eliminating the need for manual human analysis while managing complexity through modular system architecture.
Solution Approach 2:
The patent replaces manual human expertise and mechanical analysis processes with automated AI/ML-based systems. The analysis component uses machine learning models to automatically process feedback data, discover patterns, and generate migration recommendations, substituting human cognitive work with computational processes.
2Loss of time
If automated analysis components are implemented, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The analysis component performs preliminary automated analysis of the entity's computing environment and latent preferences before migration planning begins. By pre-processing data and discovering preferences automatically in advance, the system reduces the time required for subsequent migration plan development and execution.
Solution Approach 2:
The system enables self-service automation where the analysis component independently processes feedback data, discovers latent preferences, and generates migration recommendations without requiring manual human intervention at each step, thereby reducing time loss while managing complexity through autonomous operation.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products that can facilitate computing environment migration plan recommendation based on one or more latent entity computing property preferences are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover a latent computing property preference of an entity operating in a first computing environment. The computer executable components can further comprise a recommendation component that recommends a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. In some embodiments, the recommendation component recommends discovered latent computing property preferences of the entity to construct the computing environment migration plan.