Language Model Transformer for Cross-Platform Config Conversion
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Solution Overview
Problem
Existing network configuration migration tools require extensive manual intervention, deep expertise, and are inflexible, failing to efficiently adapt configurations to new platforms and integrate unsupported protocols without custom coding.
Innovation Solution
A computing system utilizing a language model transformer for cross-platform configuration conversion, which preprocesses network configurations, generates prompts based on additional data, and adapts unsupported protocols to supported alternatives, maintaining common elements and optimizing network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static tools are used for network configuration conversion, then the conversion process can be automated to some extent, but the tools lack flexibility to incorporate specific operational requirements or adapt to unique network architectures without extensive custom coding
Solution Approach 1:
The patent introduces an intermediary system comprising a data repository, preprocessing module, and language model that mediates between the source network configuration and target platform. This intermediary layer enables flexible adaptation to specific operational requirements without requiring custom coding for each scenario, as the language model can process and adapt configurations based on the enriched data from the repository and preprocessing stage.
Solution Approach 2:
The system performs preliminary actions by obtaining additional network data from devices and a data repository before the actual configuration conversion. The preprocessing module enriches the source configuration data with contextual information in advance, enabling the language model to handle specific operational requirements and unique network architectures during the conversion process without requiring post-hoc custom coding.
2Reliability
If manual intervention and deep expertise are used for platform configuration conversion, then accurate adaptation to unsupported protocols can be achieved, but the process requires significant time investment and is inefficient
Solution Approach 1:
The system enables self-service by using a language model that automatically processes and adapts network configurations from unsupported protocols to target platforms. The model independently analyzes the source configuration, consults the enriched data from preprocessing, and generates adapted configurations without requiring manual expert intervention, thereby maintaining reliability while dramatically reducing time investment.
Solution Approach 2:
The system applies parameter changes by transforming configuration parameters and protocols from the source platform to compatible parameters on the target platform. The language model identifies unsupported protocols and automatically adjusts relevant configuration parameters to supported alternatives, ensuring accurate adaptation while eliminating the need for time-consuming manual expert analysis.
3Ease of manufacture
If existing conversion tools are used, then basic configuration translation can be performed, but they fail to efficiently adapt configurations to new platforms and integrate unsupported protocols without custom coding
Solution Approach 1:
The system achieves universality by designing a multi-functional language model that can perform basic configuration translation while simultaneously adapting to new platforms and handling unsupported protocols. The model serves multiple functions including protocol conversion, parameter adaptation, and configuration optimization within a single unified system, eliminating the need for separate custom coding solutions for different scenarios.
Data Source
AI summary
A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.