Cloud Resource Capacity Testing Through Migrated Sub-Environments

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Solution Overview

Problem

Existing systems face challenges in efficiently managing and predicting resource capacity in large-scale cloud infrastructure due to cumbersome manual processes, inaccuracies, and inefficiencies, which affect performance and adaptability.

Innovation Solution

A system and method for facilitating testing within sub-environments by collecting, consolidating, and migrating resource data composites, allowing for the creation of sub-environments based on resource data, and executing migration plans to test in a destination schema.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual processes are used for resource capacity management, then human control and understanding are maintained, but efficiency and accuracy deteriorate due to cumbersome operations and calculation errors

Engineering Contradiction:

Improveresource capacity management efficiencyVSAvoiddata accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces manual mechanical processes (spreadsheets, manual data collection) with an automated computer-based system that collects, consolidates, and processes resource capacity data automatically. This substitution eliminates human error in calculations while maintaining data accuracy through systematic automated processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service automation where the computer automatically performs data collection from multiple sources, consolidates resource capacity data, generates reports, and updates documentation without requiring manual human intervention for each task, thereby improving both efficiency and accuracy.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated systems are implemented for resource capacity management, then efficiency and accuracy improve, but system complexity increases

Engineering Contradiction:

Improvedata processing speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the resource capacity management system into distinct functional modules: data collection from multiple sources, data consolidation and processing, report generation, and documentation updates. This segmentation manages complexity by organizing functions into separate, manageable components that can operate independently.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements a universal automated platform that handles multiple resource types (compute, storage, network) and performs multiple functions (data collection, consolidation, analysis, reporting) through a single integrated system, reducing overall complexity compared to separate systems for each function.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If comprehensive resource data is collected and managed, then decision-making accuracy improves, but data management complexity and storage requirements increase

Engineering Contradiction:

Improveresource capacity measurement accuracyVSAvoiddata management complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts and consolidates resource capacity data from multiple disparate sources into a single centralized system. This extraction process separates the data collection and management functions from the operational systems, reducing data management complexity while maintaining comprehensive and accurate resource capacity information.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12430300B1Systems and methods to facilitate testing within sub-environments with respect to resource capacity data corresponding to cloud resources

Publication Date: 2025.09.30 THE HUNTINGTON NAT BANK

AI summary

Systems, methods, and non-transitory, machine-readable media may facilitate testing within sub-environments with respect to resource capacity data corresponding to resources. Specifications of resource allocations for resources may be collected. Observation data, including resource metrics data, may be consolidated and used to develop resource data composites with mapped resource metrics data. A sub-environment interface may be configured to allow creation of sub-environments based on the resource data composites. Source schemas and data structures corresponding to the resource data composites may be selected. A migration plan to migrate the selected data structures to a destination schema for testing in a sub-environment may be created. A migration according to the migration plan may be executed. The destination schema may be created to match the selected source data structures and the selected source schemas. The selected data structures may be migrated to the destination schema to allow for testing in the sub-environment.