Capacity-Based Enterprise Architecture Generation Using Machine Learning
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Solution Overview
Problem
Existing enterprise network architectures are static and outdated, failing to adapt to technological advancements and changes in network demands, making them inefficient and costly to maintain.
Innovation Solution
A cloud-based system utilizing machine learning models trained with supervised, unsupervised, and statistical algorithms to analyze enterprise networks, generate health scores, and provide dynamic recommendations for resource utilization and architecture adjustments based on historical data and real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static blueprints are used for enterprise network design, then initial architecture can be established, but the architecture becomes outdated and inefficient over time
Solution Approach 1:
The patent implements dynamic architecture generation that continuously evolves enterprise network designs based on real-time data collection, machine learning analysis, and automated provisioning. Instead of static blueprints, the system creates living architectures that adapt to technological advancements and changing business requirements through ongoing monitoring and automated updates.
Solution Approach 2:
The system incorporates continuous feedback loops where network performance data, utilization metrics, and emerging technology trends are collected, analyzed by machine learning models, and used to automatically generate architecture recommendations and provisioning updates, enabling the architecture to learn and adapt over time.
2Productivity
If manual architecture updates are performed, then control over changes is maintained, but the process is time-consuming and costly
Solution Approach 1:
The system enables self-service automated provisioning where machine learning models autonomously analyze network data, generate architecture optimization recommendations, and automatically provision resources without manual intervention. The system serves itself by continuously monitoring, learning, and implementing improvements while maintaining audit trails and approval workflows for governance.
Solution Approach 2:
The patent replaces manual mechanical processes of architecture review and provisioning with automated computational systems using machine learning algorithms, data analysis engines, and automated provisioning tools that rapidly evaluate and implement architecture optimizations.
3Reliability
If static enterprise architectures are maintained, then stability is preserved, but efficiency and performance deteriorate over time
Solution Approach 1:
The system implements continuous architecture optimization through ongoing data collection, real-time analysis, and continuous provisioning adjustments. Rather than periodic updates, the system maintains continuous useful action by constantly monitoring network performance and automatically adapting architecture to maintain optimal reliability and performance.
Data Source
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AI summary
The present disclosure is directed to systems and methods for generating an enterprise architecture for an enterprise network. As one example, a method may include: receiving historical information from a plurality of enterprise networks, the historical information comprising information about an enterprise architecture of each of the enterprise networks; analyzing the historical information from the plurality of enterprise networks to generate a network health score for each of the plurality of enterprise networks; training a machine learning model using a plurality of machine learning algorithms based on the historical information and the network health score of each the plurality of enterprise networks; and generating, using the machine learning model, an enterprise architecture for a first enterprise network, the first enterprise network being a new enterprise network or an existing enterprise network from among the plurality of enterprise networks.