Mapping Translation Engine for Dynamic Network Model Deployment
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Solution Overview
Problem
Deploying customized data models to network resources often requires manual collaboration between users and technicians, limiting efficiency and scalability in network management.
Innovation Solution
A method for dynamically extending network device capabilities by generating a mapping package based on user-defined data model definitions, which is then deployed to network devices through a mapping translation engine, allowing seamless integration and execution of customized data models as overlays or extensions to existing models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual collaboration between users and technicians is used to deploy customized data models, then deployment accuracy and compatibility are improved, but deployment time and operational complexity increase
Solution Approach 1:
The system enables self-service deployment by allowing users to independently upload, validate, and deploy customized data models through automated processes. The validation engine automatically checks model compatibility and generates deployment packages without requiring technician intervention, thus reducing deployment time while maintaining accuracy through systematic validation.
Solution Approach 2:
The system performs preliminary validation and compatibility checking before actual deployment. The validation engine pre-processes uploaded models to ensure they meet requirements and are compatible with target network devices, preventing deployment failures and reducing the need for manual troubleshooting during the actual deployment phase.
2Reliability
If manual deployment processes are used, then model compatibility and feature support are verified, but productivity and scalability deteriorate
Solution Approach 1:
The automated validation engine performs self-service compatibility verification by automatically analyzing uploaded data models against predefined schemas and network device capabilities. This eliminates the need for manual compatibility checking by technicians while ensuring thorough validation, thus improving productivity without sacrificing reliability.
Solution Approach 2:
The system provides automated feedback during the validation process, informing users of compatibility issues, required modifications, and deployment readiness status. This immediate feedback loop enables users to correct issues before deployment, ensuring model compatibility while maintaining high productivity through automated rather than manual verification processes.
3Productivity
If customized data models are deployed without automated validation, then deployment speed increases, but model accuracy and feature support decrease
Solution Approach 1:
The validation engine performs preliminary accuracy verification of customized data models before deployment by checking against predefined schemas, data types, and network device capability requirements. This pre-deployment validation ensures model accuracy is verified automatically, enabling fast deployment without sacrificing precision through manual checking.
4Adaptability or versatility
If existing network device capabilities are extended with customized models, then functionality and versatility improve, but system complexity increases
Solution Approach 1:
The system segments the data model deployment process into distinct components: model upload, validation, mapping package generation, and deployment. This segmentation allows customized models to be extended and validated independently before integration with existing network device capabilities, managing system complexity through modular processing while enhancing versatility.
Solution Approach 2:
The mapping translation engine acts as an intermediary between customized data models and network device interfaces. It automatically generates mapping packages that translate custom model definitions into device-specific configurations, enabling functionality extension without directly complicating the network device architecture itself.
Data Source
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AI summary
A data model can be customized by a user and executed in real-time at a network device. The user provides definitions for the customized data model based on a data model locally stored on the network device. The user provided definitions are used to generate a mapping contract which is processed by a mapping package generator to generate a mapping package. The mapping package can then be processed by a translation engine to dynamically execute a customized data model in real-time.