Cloud Resource Utilization Tool for Cost Prediction
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Solution Overview
Problem
Current systems fail to accurately estimate and manage cloud resource utilization and licensing costs, especially during operational changes such as expansion or contraction, and do not consider geographical implications or use automated machine learning for forecasting.
Innovation Solution
A system utilizing machine learning to model and predict resource utilization based on historical data, incorporating geographic and licensing factors, which automatically configures changes in the cloud environment to match operational scenarios and estimate costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation methods are used to estimate cloud resource costs, then simplicity is maintained, but accuracy and comprehensiveness of cost estimation deteriorates
Solution Approach 1:
The patent introduces an automated modeling tool as an intermediary between manual calculation methods and cloud resource cost estimation. This tool serves as a mediator that handles the complex calculations, data integration, and scenario analysis, while users interact through a simplified interface. The tool automatically collects resource usage data, applies pricing models, and generates cost estimates without requiring users to manually complex calculations.
Solution Approach 2:
The system implements self-service by automatically gathering resource usage data from cloud environments, storing it in databases, and performing cost calculations without continuous human intervention. The automated modeling tool continuously monitors resource consumption, updates cost estimates based on current usage patterns, and generates reports independently, reducing the need for manual data collection and analysis while improving accuracy.
2Adaptability or versatility
If cloud resource allocation is increased to support operational expansion, then business growth capability is improved, but operational costs increase
Solution Approach 1:
The patent applies dynamics by enabling flexible, scenario-based resource allocation that adapts to different operational conditions. The system allows users to model various scenarios (expansion, contraction, geographic changes) and dynamically adjust resource allocation based on predicted outcomes. This dynamic approach enables businesses to optimize resource levels for each scenario, avoiding over-provisioning while maintaining the ability to scale when needed.
Solution Approach 2:
The system performs preliminary action by using automated modeling to predict resource requirements and costs before actual operational changes are implemented. Users can simulate expansion or contraction scenarios in advance, evaluate cost implications, and make informed decisions about resource allocation. This preliminary analysis prevents unnecessary resource increases and helps identify cost-effective optimization opportunities.
3Measurement precision
If comprehensive data collection and automated modeling are implemented, then forecasting accuracy is improved, but data processing requirements and system resources increase
Solution Approach 1:
The patent extracts and separates different components of the data processing system to manage complexity. It divides the system into distinct modules: data collection from cloud environments, storage in databases, automated modeling for analysis, and reporting. This extraction allows each component to be optimized independently and enables selective data processing based on specific forecasting needs, reducing unnecessary data processing load while maintaining accuracy.
Data Source
AI summary
A system and method are proposed that allows for predictive analysis to model Information Technology costs, such as hosting costs, in various scenarios, such as, reducing/expanding workforce, shutting locations, or adding locations. The system, in performing optimization functions, gathers data from various locations and providers and normalizes the data in data structures to allow for direct comparison of costs with calculated data needs, which allows for automatic optimization of the organization's Information Technology resources.