ML Confidence Score for Cloud Infrastructure Dependency Validation
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Solution Overview
Problem
Current systems lack comprehensive validation of resource deployments across multiple cloud providers, leading to potential failures and associated losses due to unmet dependencies, resulting in wasted time, effort, and financial resources.
Innovation Solution
A machine learning predictive model is trained with historic infrastructure deployment data to generate a confidence score for the likelihood of successful deployment, enabling pre-deployment validation and dynamic implementation of resource provisioning across multiple providers, ensuring all dependencies are met before actual deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deployment validation is performed without comprehensive dependency checking, then deployment speed is improved, but deployment reliability deteriorates due to unmet dependencies
Solution Approach 1:
The system performs preliminary validation of deployment dependencies using a machine learning model trained on historical deployment data before actual deployment execution. The model generates a confidence score indicating the likelihood of successful deployment, allowing the system to validate dependencies in advance without performing full deployment, thus resolving the contradiction between deployment speed and reliability.
2Reliability
If comprehensive dependency validation is performed before deployment, then deployment reliability is improved, but time consumption increases
Solution Approach 1:
The system uses a machine learning model that has been trained on historical deployment data to create a virtual representation of deployment outcomes. Instead of performing actual deployment to validate dependencies, the model generates predictions based on patterns learned from historical data, significantly reducing validation time while maintaining high reliability.
3Measurement precision
If manual dependency validation is performed, then measurement precision is improved, but device complexity increases due to additional validation systems
Solution Approach 1:
The system replaces manual dependency validation processes with an automated machine learning model. The model has been trained on historical deployment data and automatically analyzes deployment topologies to predict success likelihood, eliminating the need for complex manual validation procedures while maintaining or improving validation accuracy.
4Productivity
If deployment proceeds without confidence score validation, then productivity is improved, but loss of resources increases due to failed deployments
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model analyzes historical deployment outcomes and continuously improves its predictions. The confidence score generated by the model provides feedback on deployment viability before resources are committed, allowing the system to avoid failed deployments and reduce resource waste while maintaining high productivity.
Data Source
AI summary
Systems and methods enable optimized infrastructure deployment planning and validation. In embodiments, a method includes: training, by a computing device, a machine learning (ML) predictive model with historic infrastructure deployment data of a plurality of resource providers in a network environment, including resource dependencies; generating, by the computing device, a deployment topology for requested resources of an information technology (IT) deployment request of a user; generating, by the computing device using the ML predictive model, a confidence score regarding a likelihood of successful implementation of the deployment request based on dependencies of the deployment topology; and dynamically implementing, by the computing device, deployment of the IT deployment request to provision the requested resources from multiple providers in the network environment based on the confidence score.