Multi-Vendor Network Fabric Digital Twin for Intent Validation
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Solution Overview
Problem
Traditional manual network configuration methods lead to inefficiencies, higher chances of human error, and slower response times due to the lack of automated generation and validation of network digital twins based on intent.
Innovation Solution
A system and method for generating multi-vendor network fabric digital twins based on intent, using a Fabric Manager to ingest configuration inputs, create artifacts, and deploy a staging network for testing and validation before actual deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration methods are used for network management, then network administrators can directly deploy configurations, but this leads to higher chances of human error and slower response times
Solution Approach 1:
The system performs preliminary actions by generating a digital twin and executing NetDevOps validation (including syntax checks, semantic validation, and compliance verification) before the actual network configuration is deployed. This advance validation prevents errors from reaching production, thereby improving configuration accuracy without significantly increasing deployment time.
Solution Approach 2:
The system creates a digital twin (a copy) of the network configuration in a virtual environment. This copy allows administrators to validate and test configurations before deploying to the actual network, reducing human error while maintaining efficient deployment processes through automated validation pipelines.
2Reliability
If automated NetDevOps validation is implemented before deployment, then configuration reliability improves, but the process complexity increases
Solution Approach 1:
The validation process is segmented into distinct, modular stages: syntax validation, semantic validation, compliance checking, and deployment simulation. Each stage handles a specific aspect of configuration validation, making the overall complex process manageable and maintainable through clear separation of concerns.
Solution Approach 2:
The digital twin serves as an intermediary between configuration creation and actual deployment. It absorbs the complexity of validation processes by providing a virtual testing environment, allowing automated NetDevOps workflows to operate without directly complicating the deployment system itself.
3Reliability
If digital twin generation and NetDevOps validation are added to the workflow, then human error is reduced, but the overall process time increases
Solution Approach 1:
The system maintains continuity of useful action by running validations in parallel where possible and using automated workflows that continuously process configurations through the validation pipeline without manual intervention. This keeps the error reduction benefits while minimizing delays to deployment speed.
4Adaptability or versatility
If multi-vendor support is implemented in the digital twin system, then system versatility improves, but the complexity of managing different vendor configurations increases
Solution Approach 1:
The digital twin system is designed with universal, vendor-agnostic data models and validation rules that can handle multiple vendor configurations through a unified interface. This allows the system to support diverse vendors without requiring separate complex management mechanisms for each vendor, maintaining versatility while controlling complexity.
Data Source
AI summary
In some aspects, the method may include receiving, by an administrator interface, configuration inputs in one or more formats. Also, the method may include ingesting, by a fabric manager of a network fabric, the received configuration inputs. Furthermore, the method may include processing, by the fabric manager, the ingested configuration inputs to create configuration artifacts. In addition, the method may include generating, by the fabric manager, a topology. Furthermore, the method may include deploying the network fabric as a staging network in cloud or on-prem servers to create a digital twin.