Cloud Sub-Environments for Resource Capacity Testing
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Solution Overview
Problem
Existing systems face challenges in efficiently managing and adapting to changing resource capacities in large-scale cloud infrastructure due to manual spreadsheet-based methods, inaccuracies, inefficiencies, and low automation, leading to performance issues and slow infrastructure adjustments.
Innovation Solution
A system and method for facilitating testing within sub-environments by collecting and consolidating resource capacity data, creating resource data composites, and using a sub-environment interface to migrate and test these data structures, enabling efficient and automated capacity management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual spreadsheet-based methods are used for capacity control, then ease of operation is maintained, but productivity and accuracy deteriorate due to cumbersome processes, calculation errors, and time-consuming manual work
Solution Approach 1:
The patent replaces manual mechanical spreadsheet operations with an automated computational system that collects resource data, performs calculations, and generates capacity reports automatically. This substitution eliminates manual errors and significantly improves productivity while managing complexity through systematic automation.
Solution Approach 2:
The system enables self-service capacity control by automatically gathering resource metrics, processing data through predefined algorithms, and generating capacity assessments without requiring manual intervention. This self-serve approach maintains ease of operation while dramatically improving productivity and accuracy.
2Measurement precision
If automated systems are implemented for capacity control, then productivity and accuracy improve, but device complexity increases due to system integration requirements
Solution Approach 1:
The patent segments the capacity control system into distinct functional modules: data collection from multiple sources, data processing and validation, capacity calculation engines, and reporting components. This segmentation improves measurement precision through specialized processing while managing complexity by creating independent, maintainable system components.
Solution Approach 2:
The system implements universal data collection and processing capabilities that can handle multiple resource types (compute, storage, network) and various data sources through a unified framework. This multi-functionality approach improves measurement accuracy across diverse resources while avoiding the complexity of separate specialized systems.
3Measurement precision
If comprehensive resource data collection is performed across large-scale infrastructure, then measurement precision improves, but loss of time increases due to the scale and complexity of data gathering
Solution Approach 1:
The patent implements preliminary actions by pre-configuring data collection agents, establishing predefined metrics and thresholds, and setting up automated data gathering processes before capacity assessments are needed. This preliminary setup enables rapid, accurate data collection when required, improving measurement precision without incurring time delays during actual assessments.
Solution Approach 2:
The system maintains continuous data collection and processing operations, constantly gathering resource metrics and updating capacity information in real-time or near-real-time. This continuous action ensures measurement precision is maintained without requiring periodic time-consuming data gathering cycles, as the system continuously monitors and processes resource data.
Data Source
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.