Migration Model for Predicting Cloud Application Success
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Solution Overview
Problem
Traditional cloud migration techniques lack optimization and are based on limited decision criteria, failing to provide a holistic approach for migrating applications from local or legacy environments to cloud environments, especially in multi-cloud scenarios.
Innovation Solution
A method and system that normalize historical key performance indicators (KPIs) to create a migration model, allowing for the prediction of successful migration probabilities and simulation-based recommendations to increase the likelihood of successful migration by adjusting key performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual migration techniques are used, then migration can be performed with simple processes, but migration success probability is low and lacks optimization
Solution Approach 1:
The system performs preliminary analysis of historical migration data and normalizes key performance indicators before actual migration to predict success probability and identify necessary modifications in advance, allowing optimization decisions to be made before the migration process begins
Solution Approach 2:
A migration model acts as an intermediary between historical data and migration decisions, normalizing various KPIs and simulating migration scenarios to provide optimized recommendations without requiring direct complex analysis of raw historical data
2Measurement precision
If comprehensive historical data analysis is performed, then migration success prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system transforms raw historical KPI data into normalized parameters with consistent scales and distributions, enabling accurate predictions while reducing computational complexity through standardization and dimensionless transformation of various metrics
Solution Approach 2:
The system extracts only the most relevant key performance indicators from comprehensive historical data through the migration model, focusing analysis on critical factors rather than processing all available data, thereby maintaining accuracy while reducing processing time
Data Source
AI summary
A computer implemented method is provided that includes using historic migration data to label key performance indicators (KPIs) in a migration model including a scale that indicates a level of successful migration to a remote provider. Employing the migration model to predict successful migration of a local application having one or more of said one or more of local key performance indicators for the local application. Migrating the local application to a remote provider when the model to predict successful migration indicates a greater than threshold value for successful migration.