AI Cloud Architecture Optimization Graph
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Solution Overview
Problem
Optimizing cloud architectures using artificial intelligence is hindered by complexity, lack of standardization across cloud platforms, dynamic nature of cloud environments, challenges in obtaining high-quality data, and the need to balance various constraints such as cost, performance, security, and compliance.
Innovation Solution
The system generates a standardized taxonomy of cloud resources and their interconnectivity, continuously adapts to changes in cloud environments, validates incoming data for accuracy, and uses artificial intelligence models trained on historical usage data to recommend optimized cloud architecture patterns based on specific requirements and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artificial intelligence models are used to optimize cloud architectures, then optimization effectiveness is improved, but device complexity increases due to the need to manage multiple cloud resources and their interconnectivity
Solution Approach 1:
The patent introduces an intermediary layer between the AI model and the complex cloud architecture. This intermediary represents the cloud architecture as a graph with standardized nodes and edges, translating complex resource relationships into a manageable format that the AI model can process effectively.
Solution Approach 2:
The patent segments the cloud architecture into discrete components (compute resources, storage, networking, databases) represented as nodes in a graph, with relationships represented as edges. This segmentation allows the AI model to analyze and optimize each component and relationship independently while maintaining overall system context.
2Adaptability or versatility
If cloud architecture patterns are standardized across multiple platforms, then adaptability is improved, but loss of information occurs due to the need to translate between different cloud provider terminologies and formats
Solution Approach 1:
The patent creates a universal cloud architecture representation framework that can model different cloud resources from multiple providers (AWS, Azure, GCP) using a common set of node types and relationship patterns. This universal model serves as a multi-functional interface that translates between various cloud provider-specific terminologies while preserving essential architectural information.
3Reliability
If real-time monitoring and analysis of cloud infrastructure is performed, then reliability is improved, but use of energy increases due to the computational intensity of continuous analysis
Solution Approach 1:
The patent implements periodic analysis of cloud architecture patterns rather than continuous real-time monitoring. The system analyzes the cloud architecture graph at scheduled intervals, updating the AI model's understanding of the infrastructure state periodically, which reduces computational energy consumption while maintaining adequate reliability for optimization purposes.
Data Source
AI summary
Systems and methods are described herein for optimizing cloud architectures using artificial intelligence models trained on standardized cloud architecture patterns corresponding to specific requirements. For example, the system may receive a first cloud architecture processing requirement. The system may receive a first set of available cloud resources. The system may generate a first feature input based on the first cloud architecture processing requirement and the first set of available cloud resources. The system may input the first feature input into a first artificial intelligence model to generate a first output. The system may determine, based on the first output, a first cloud architecture pattern for the first set of available cloud resources.