AI Quorum Provisioning Code for Multi-Cloud Deployment
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Solution Overview
Problem
Provisioning resources across diverse cloud substrates is challenging due to disparities in operational methods and interactions, requiring developers to create distinct implementations for each substrate, leading to increased complexity and overhead.
Innovation Solution
Employing an AI-driven quorum of multiple AI models to generate, verify, and audit provisioning code, ensuring consensus and rigorous testing within a sandbox environment to streamline development and improve scalability, security, and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distinct implementations are created for each cloud substrate, then resource provisioning can be performed on each substrate, but device complexity and code maintenance overhead increase
Solution Approach 1:
The patent implements a universal provisioning code that can operate across multiple cloud substrates (AWS, Azure, GCP) without requiring distinct implementations for each substrate. This single codebase achieves multi-functionality by adapting to different cloud environments, thereby reducing code complexity while maintaining broad resource provisioning capability across diverse cloud platforms
2Reliability
If multiple AI models are employed to verify and audit provisioning code, then code reliability and security improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary verification and auditing of provisioning code using multiple AI models before the code is executed in the sandbox environment. By conducting these checks in advance, the system ensures code reliability and security while establishing a clear workflow that manages the time investment required for comprehensive verification before deployment
Solution Approach 2:
Multiple AI models serve as intermediary verification layers between code generation and sandbox execution. These AI models audit and validate the provisioning code, acting as mediators that enhance reliability and security while managing the computational overhead through structured verification processes
Data Source
AI summary
An application server may receive user input indicating a plurality of provisioning parameters for provisioning resources on a cloud substrate. The application server may transmit, to a first artificial intelligence (AI) model, the plurality of provisioning parameters and a request to generate, based on the plurality of provisioning parameters, provisioning code associated with the cloud substrate. The application server may transmit, to one or more second AI models, the provisioning code generated by the first AI model, the plurality of provisioning parameters, and a request to analyze the provisioning code based on the plurality of provisioning parameters. The application server may update respective reputation values associated with the first AI model and the one or more second AI models based on one or more analysis results associated with output of the one or more second AI models.