Cloud Infrastructure Configuration for Automated Compliance Optimization
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Solution Overview
Problem
Current methods for optimizing IT system infrastructures in cloud environments are time-consuming, prone to human error, and ineffective in optimizing for specific variables, and do not allow easy migration to new cloud platforms due to manual compliance checks.
Innovation Solution
A method that models existing IT system infrastructures, generates optimized proposal configurations for specific variables, and uses a feedback loop to continuously adjust and ensure compliance with cloud environment rules, utilizing machine learning and automated validation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual compliance checks and optimization processes are used for IT infrastructure, then human control and flexibility are maintained, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system performs self-optimization by automatically generating, validating, and applying infrastructure configuration changes without human intervention. The optimization engine continuously monitors infrastructure state and autonomously executes compliance checks and configuration updates, eliminating manual operations while maintaining high accuracy through automated validation loops.
Solution Approach 2:
Manual mechanical processes (human operators performing compliance checks and optimizations) are replaced with an automated software system that uses machine learning models and rule engines to perform the same functions. This substitution eliminates human error and significantly reduces the time required for infrastructure optimization while maintaining or improving accuracy.
2Ease of manufacture
If traditional infrastructure-as-code methods are used, then infrastructure can be managed through code, but any modifications require re-running the entire compilation and execution process
Solution Approach 1:
The infrastructure-as-code system is segmented into independent, modular components that can be individually modified and validated. Instead of re-compiling and re-executing entire infrastructure definitions, the system identifies specific configuration elements that need changes, validates only those segments, and applies targeted updates. This segmentation enables incremental modifications without requiring full system re-processing.
Solution Approach 2:
The system transitions from static, all-or-nothing infrastructure updates to dynamic, incremental modifications. The optimization engine continuously monitors infrastructure state and applies changes in real-time based on current conditions, allowing flexible adjustments without requiring complete re-execution of infrastructure code. This dynamic approach improves modification efficiency while maintaining infrastructure integrity.
3Adaptability or versatility
If infrastructure optimization focuses on multiple variables simultaneously, then comprehensive optimization is achieved, but the complexity of managing trade-offs increases
Solution Approach 1:
The system manages multi-variable optimization by dynamically adjusting parameters and their priority weights based on current infrastructure state and organizational goals. The optimization engine allows flexible reconfiguration of which variables are optimized and their relative importance, enabling comprehensive optimization scope while managing complexity through parameter-based control rather than fixed complex algorithms.
Solution Approach 2:
The system uses continuous feedback loops to monitor infrastructure performance across multiple variables and automatically adjust optimization strategies. By implementing feedback mechanisms that track the impact of changes on various parameters, the system can manage trade-offs between competing objectives without requiring complex manual intervention, thereby maintaining adaptability while controlling system complexity.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for a method of cloud infrastructure optimization. The method identifies an existing infrastructure configuration deployed in a cloud environment and generates a plurality of proposal configurations, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. The method selects a proposal configuration from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration, and the selected proposal configuration is deployed in the cloud environment. The method then analyzes the selected proposal configuration for a level of adjustment for the at least one variable. The method trains a model engine with existing and new training data.