AI Cloud Infrastructure Planning Assistant
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Solution Overview
Problem
Current cloud infrastructure planning relies heavily on human judgment, leading to inaccuracies, bottlenecks, and a lack of learning from past projects, resulting in imperfect resource allocation and potential overspending.
Innovation Solution
A system utilizing artificial intelligence and machine learning agents to independently and concurrently develop cloud infrastructure plans, incorporating historical data to improve accuracy, precision, and speed by generating site solutions, plans of record, execution designs, and resource predictions based on capacity, infrastructure, and availability correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human decision-makers perform planning cycles manually, then organizational control and judgment are maintained, but accuracy, precision, and speed of planning are reduced
Solution Approach 1:
The patent replaces human mechanical decision-making processes with an automated AI-based planning system. The system uses machine learning models to automatically perform capacity planning, resource allocation, and infrastructure design tasks that were previously performed manually by human planners, thereby increasing both speed and accuracy simultaneously.
Solution Approach 2:
The AI planning system operates autonomously to generate capacity plans without requiring continuous human intervention. The system self-manages the planning cycle by automatically analyzing historical data, forecasting future needs, and generating optimized resource allocation plans, freeing human operators from repetitive manual planning tasks.
2Reliability
If human decision-makers perform planning cycles, then organizational context is considered, but bottlenecks occur and learning from past projects is not consistently applied
Solution Approach 1:
The system incorporates feedback loops that continuously learn from historical planning data and actual outcomes. Machine learning models are trained on past project results, enabling the system to automatically apply lessons learned from previous planning cycles and continuously improve future plans, eliminating the inconsistency of manual knowledge transfer.
Solution Approach 2:
The AI system performs preliminary analysis of historical data and patterns before generating new capacity plans. By pre-processing and learning from past projects in advance, the system can quickly generate accurate plans without requiring time-consuming manual review of historical context during each new planning cycle.
3Adaptability or versatility
If cloud infrastructure planning is approached as independent projects, then project-specific customization is achieved, but learning from past projects is not consistently considered
Solution Approach 1:
The AI planning system serves multiple projects simultaneously while maintaining the ability to customize plans for each specific project. The system processes individual project requirements through standardized machine learning models that have been trained on aggregated historical data from all projects, enabling both customization and consistent application of historical lessons.
Solution Approach 2:
The system nests individual project-specific planning within a broader framework of organizational historical data. Each project plan is generated by the AI model that has been trained on and contains knowledge from all previous projects, effectively nesting the specific project needs within the cumulative organizational learning repository.
Data Source
AI summary
Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.