Cloud Migration Optimization Using Topic Modeling
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Solution Overview
Problem
Current tools for managing distributed computing environments, including cloud computing, lack a framework to optimize migration decisions and align with client constraints effectively, relying on manual data sources and failing to consider non-functional requirements and business objectives.
Innovation Solution
A computer-implemented method using topic modeling, latent semantic analysis, and deterministic optimization to identify homogenous applications, calculate disposition metrics, and predict resource dispositions, which includes selecting training data, calculating weights, and recommending resource dispositions such as retire, retain, or re-engineer, while considering client constraints and non-functional requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual data sources and traditional management tools are used, then implementation simplicity is maintained, but the ability to optimize migration decisions and align with client constraints deteriorates
Solution Approach 1:
The patent replaces manual data collection and traditional management tools with automated machine learning systems. The system automatically ingests data from multiple sources including application logs, observability data, and configuration management databases, eliminating the need for manual data gathering while enabling sophisticated migration decision optimization through algorithms that analyze business value, technical constraints, and migration priorities.
2Measurement precision
If comprehensive data analysis using topic modeling and latent semantic analysis is performed, then migration decision accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex data analysis process into distinct modular components: data ingestion from multiple sources, topic modeling to identify application themes and patterns, latent semantic analysis to uncover hidden relationships, and disposition prediction using machine learning models. Each module processes specific aspects of the data independently, then integrates results to produce comprehensive migration recommendations, making the overall complex system manageable and maintainable.
3Productivity
If automated machine learning models are used to predict resource dispositions, then analysis speed and objectivity are improved, but interpretability and control deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the system generates disposition predictions for applications (such as migrate, retire, or modernize), then provides detailed explanations and reasoning for each prediction based on the analyzed data. The system considers multiple factors including business value, technical constraints, and migration priorities, presenting this information in a structured manner that helps stakeholders understand and validate the automated recommendations before implementation.
Data Source
AI summary
Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that ingests data from one or more computing environments, where the data is related to applications. The processor(s) identifies, based on utilizing topic modeling and latent semantic analysis of the data, homogenous applications among the applications, which include analyzing subdata handled by each application and functionalities of each application; the homogenous applications comprise similarities in the data and in the functionalities. The processor(s) determines overlapping data among the homogenous applications based on the topic modeling, the latent semantic analysis, and term frequency-inverse document frequency of terms in the overlapping data. The processor(s) selects, from the overlapping data, training data. The processor(s) utilizes the training data to calculate weights for disposition metrics and the metrics to predict the resource dispositions for the applications.