Network Configuration Assets for Runtime IPU Reconfiguration
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Solution Overview
Problem
Existing network architectures face challenges in accurately establishing performance and information flows of network devices after deployment, leading to inefficiencies and service quality issues, necessitating dynamic reconfiguration mechanisms based on the behavior of deployed infrastructure processing units (IPUs).
Innovation Solution
Utilizing AI techniques for software-defined networking (SDN) to perform dynamic analysis of network configurations, generate functionality tags for grouped devices, determine fitness and error indices, and automatically generate configuration assets using large language models (LLMs) to optimize network device performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network devices are deployed based on theoretical design, then initial configuration can be established, but the actual performance and information flows cannot be accurately established after deployment
Solution Approach 1:
The patent implements feedback mechanisms by collecting actual runtime data from deployed network devices and using AI models to compare theoretical configurations with observed behavior. This feedback loop enables continuous refinement of configuration assets based on real performance metrics and information flow patterns, resolving the contradiction between measurement accuracy and operational ease.
Solution Approach 2:
The system enables self-service by automatically generating and refining configuration assets through AI-driven analysis of deployed device behavior. Rather than requiring manual configuration adjustments, the system autonomously learns from runtime data and generates optimized configurations, improving both measurement accuracy and operational simplicity.
2Adaptability or versatility
If static configuration assets are used for network devices, then deployment is simplified, but dynamic adaptation to actual behavior is lost
Solution Approach 1:
The patent transforms static configuration assets into dynamic ones by implementing AI models that continuously learn from runtime behavior of network devices. The system adapts configurations based on observed information flows and performance metrics, enabling dynamic adjustment while managing complexity through automated machine learning processes rather than manual intervention.
Solution Approach 2:
The system performs preliminary action by pre-training AI models on theoretical network designs before deployment. These pre-trained models serve as initial configuration assets that can be quickly adapted to actual deployed behavior, reducing the complexity of real-time adaptation while maintaining high adaptability to actual device performance.
3Productivity
If manual analysis and reconfiguration of network devices is performed, then service quality issues can be addressed, but time loss and inefficiency increase
Solution Approach 1:
The patent replaces manual mechanical analysis and reconfiguration processes with AI-driven automated systems. Machine learning models automatically analyze runtime data, identify performance issues, and generate reconfiguration assets without human intervention, dramatically improving productivity while minimizing time loss compared to manual network device management.
Data Source
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AI summary
Methods, apparatus, systems, and articles of manufacture to manage configuration assets for network devices are disclosed. Example instructions cause at least one programmable circuit to generate network infrastructure instructions using a model, the network infrastructure instructions based on a configuration request and on a network infrastructure; and deploy a program corresponding to the network infrastructure instructions to at least one device in the network infrastructure.