Network Device Onboarding via YANG-Based Configuration Generation
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Solution Overview
Problem
The existing process of onboarding devices into networks is lengthy and prone to human error due to the manual entry of configuration data, particularly in generating CMDB and JAR files, which is time-consuming and requires careful review.
Innovation Solution
Derive configuration parameters from a manufacturer's pre-existing YANG file to automatically generate CMDB and JAR files, reducing manual data entry and minimizing errors, with dynamic parameters entered manually when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual entry of configuration data is used to generate CMDB and JAR files, then configuration accuracy can be maintained through careful review, but the process is lengthy and time-consuming
Solution Approach 1:
The system automatically copies configuration parameters from the manufacturer's pre-existing YANG file to generate CMDB and JAR files. This copying process eliminates manual data entry while maintaining accuracy through automated extraction of device model information, interface configurations, and protocol parameters directly from the source YANG file structure.
Solution Approach 2:
The manual mechanical process of copying and entering configuration data is replaced with an automated computational system. The processor automatically extracts parameters from the YANG file, populates the parameter database, generates the parameter library, creates class definitions, and produces CMDB and JAR files through software automation, eliminating the need for manual review and entry.
2Adaptability or versatility
If manual data entry is used for generating configuration files, then flexibility in handling dynamic parameters is maintained, but human error increases and productivity decreases
Solution Approach 1:
The configuration generation process is segmented into distinct automated stages: extracting parameters from the YANG file, populating the parameter database, generating the parameter library, creating class definitions, and producing final configuration files. This segmentation allows systematic automation of static parameters while preserving manual input capability for dynamic parameters, reducing errors and improving productivity.
Solution Approach 2:
The system dynamically adapts its operation mode based on parameter type. Static parameters are automatically extracted and processed from the YANG file, while dynamic parameters can be manually entered when necessary. This dynamic approach maintains flexibility for handling different parameter types while maximizing automation for routine configurations, thereby improving productivity without sacrificing adaptability.
3Loss of time
If automated generation of configuration files from YANG data is implemented, then time expenditure and human error are reduced, but the complexity of the configuration system increases
Solution Approach 1:
The system employs a universal automated configuration generation platform that handles multiple functions: parsing YANG files, extracting parameters, populating databases, generating libraries, creating class definitions, and producing various configuration files (CMDB, JAR). This multi-functional system consolidates what would otherwise require multiple separate tools and manual processes, reducing overall system complexity despite the automation capabilities.
Solution Approach 2:
The patent introduces intermediary components such as the parameter database file, parameter library, and class definition file that mediate between the source YANG file and the final configuration outputs. These intermediaries structure and organize configuration data in standardized formats, simplifying the automation process and making the system more manageable despite the increased functionality required for automated generation.
Data Source
AI summary
A system and method are provided to configure a network for onboarding of devices. A hierarchical parameter data file describing configuration parameters of the plurality of devices, such as a Yet Another Next Generation (YANG) model, is received. A parameter database file including a plurality of configuration parameters is populated, with at least one of the configuration parameters being derived from content of the hierarchical parameter data file, and a parameter library is generated based on the parameter database file. A class definition file is generated based on at least one of the database file and the hierarchical parameter data file. Then, for a network function, a corresponding configuration file is generated based on the parameter library and the class definition file, and is provided to the network function for implementation.