Automated Networked Data Center Architecture Modeling
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Solution Overview
Problem
Identifying the best data center service options for building out an environment is time-consuming and often results in suboptimal selections, highlighting the need for improved methods and systems to model, build out, and activate data center environments.
Innovation Solution
A method that automatically determines a networked data center architecture by assembling a database of data center capabilities, receiving customer requirements, searching for solutions that satisfy these requirements, and outputting recommendations. This method also includes visualizing options and making API calls to provision the architecture, with the aid of multimodal AI for data analysis and action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify data center service options, then customer can evaluate and select services, but the process is time-consuming and results in suboptimal selections
Solution Approach 1:
The patent replaces manual customer evaluation processes with an automated AI system that uses machine learning models to analyze data center capabilities, requirements, and compatibility. The system automatically generates recommendations, solutions, and visualizations without requiring manual customer effort, thereby eliminating time loss while maintaining or improving selection quality through sophisticated algorithms.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between customer requirements and data center capabilities. This intermediary automatically processes information, performs compatibility analysis, and generates optimized recommendations, resolving the contradiction by providing high-quality selections without requiring time-consuming manual evaluation by customers.
2Reliability
If comprehensive data center capabilities are modeled, then optimal solutions can be identified, but system complexity increases
Solution Approach 1:
The patent segments the complex modeling system into distinct modular components: a database module for storing data center capabilities, a requirements module for capturing customer needs, a search module for finding matching solutions, and a visualization module for presenting recommendations. This segmentation reduces overall system complexity while maintaining comprehensive modeling capabilities through organized, manageable modules.
Solution Approach 2:
The patent creates a universal AI-based platform that handles multiple functions including data storage, requirement analysis, solution searching, recommendation generation, and visualization. This multi-functional system reduces complexity by consolidating diverse capabilities into a single integrated platform rather than requiring separate systems for each function.
3Productivity
If automated provisioning is implemented, then customer lifecycle is streamlined, but automation extent increases system complexity
Solution Approach 1:
The patent implements preliminary action by pre-modeling data center capabilities in a structured database and pre-establishing the framework for automated analysis and provisioning. This preparation enables rapid automated provisioning when customers submit requirements, achieving high productivity while managing complexity through advance setup of the automation infrastructure.
Data Source
AI summary
In an embodiment, a method automatically determines a networked data center architecture. In the method, a database describing capabilities of a data center provider is assembled. The database describes capabilities of a plurality of data centers of the data center provider. A specification of requirements for the networked data center architecture is received. The specification describes data processing and connectivity requirements of a customer of a data center provider. The database is searched to determine a solution including a plurality of connections and data center that satisfy the specification. Based on the searching, the solution is output as a recommendation to provide the networked data center architecture. In another embodiment, options for a networked data center architecture are visualized. In yet another embodiment, API calls are made to provision the networked data center architecture.