Cloud Infrastructure Configuration for Automated Compliance Optimization
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Solution Overview
Problem
Current methods for optimizing IT system infrastructure 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 improve and maintain compliance with cloud environment rules, utilizing machine learning and automated validation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to optimize IT system infrastructure in cloud environments, then human control and understanding are maintained, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system performs self-optimization of cloud infrastructure by automatically generating, evaluating, and applying configuration changes without human intervention. The optimization engine continuously monitors infrastructure performance and autonomously implements improvements, eliminating the need for manual optimization processes while maintaining high accuracy through automated validation mechanisms.
Solution Approach 2:
Manual mechanical processes of infrastructure optimization are replaced with an automated software-based optimization engine that uses machine learning models and algorithms to analyze infrastructure configurations, generate optimization proposals, and implement changes. This substitution eliminates human error and significantly reduces optimization time while maintaining or improving accuracy.
2Reliability
If traditional infrastructure-as-code methods are used, then compliance with cloud environment rules can be maintained, but modifications require re-running the entire compiler and execution engine process
Solution Approach 1:
The infrastructure optimization process is segmented into independent modular components: configuration analysis, proposal generation, validation, and implementation. Each modification can be processed independently through targeted validation checks rather than requiring complete re-compilation and re-execution of the entire infrastructure-as-code process, significantly improving modification efficiency while maintaining compliance through focused validation of affected segments.
Solution Approach 2:
The system performs preliminary validation and compliance checks on proposed infrastructure modifications before actual implementation. By pre-evaluating changes against cloud environment rules and constraints, the system ensures compliance is maintained while avoiding the need to re-run entire compilation and execution processes, as the preliminary action has already verified the validity of modifications.
3Reliability
If comprehensive validation is performed on all infrastructure changes, then compliance with cloud rules is ensured, but the validation process becomes complex and time-consuming
Solution Approach 1:
The validation process applies partial validation focused only on the specific infrastructure components and rules affected by each modification, rather than performing exhaustive validation on the entire infrastructure. This targeted approach ensures compliance assurance for affected areas while significantly reducing validation complexity and time by avoiding unnecessary validation of unrelated system components.
Solution Approach 2:
Compliance rules and validation criteria are pre-configured and stored in the system before infrastructure modifications occur. The validation engine has pre-loaded knowledge of cloud environment rules, constraints, and best practices, enabling rapid validation of modifications without requiring complex real-time analysis, thus ensuring compliance while simplifying the validation process.
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.