Legacy WAN Transformation Planning for SD-WAN Migration
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Solution Overview
Problem
The planning and design phase of transitioning from legacy networks to SD-WAN is error-prone, time-consuming, and costly for Communication Service Providers (CSPs) and System Integrators (SIs, leading to delays and increased expenditures.
Innovation Solution
A method and system utilizing data analysis through statistical and machine learning techniques to identify transformation requirements, determine configuration parameters, and generate design templates for transforming legacy WAN to SD-WAN, with automated deployment and integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data collection and planning is used during network transformation, then flexibility in customizing solutions is maintained, but the process becomes error-prone, time-consuming, and cumbersome
Solution Approach 1:
The system enables self-service automation where the network transformation platform automatically collects data from multiple sources, analyzes requirements, determines configuration parameters, and generates designs without requiring extensive manual intervention. The system serves itself by automating the planning process while maintaining solution customization through automated policy application.
Solution Approach 2:
The patent replaces manual mechanical planning processes with automated computational systems. Statistical and machine learning algorithms substitute for manual data analysis, while automated configuration generation replaces manual design creation. This substitution eliminates human errors and significantly reduces planning cycle duration.
2Productivity
If automated data analysis and design generation is implemented, then planning cycle time is reduced and errors are minimized, but complexity of the system increases
Solution Approach 1:
The system segments the complex network transformation process into distinct modular components: data collection module, data analysis module, configuration determination module, and design generation module. Each module handles a specific function independently, making the overall complex system manageable and easier to implement while maintaining high productivity.
Solution Approach 2:
The platform is designed as a universal system that can handle multiple network transformation tasks through a single integrated architecture. The same core system performs data collection, analysis, configuration determination, and design generation for various network scenarios, reducing overall system complexity compared to multiple specialized systems.
3Manufacturing precision
If traditional plan and design cycles are used, then detailed customization of network solutions is achieved, but transformation timelines are impacted and revenue leakage occurs
Solution Approach 1:
The system performs preliminary actions by automatically analyzing existing network data, identifying transformation requirements, and pre-determining configuration parameters before the actual network transformation begins. This preliminary automated planning eliminates time-consuming manual cycles while maintaining solution accuracy through automated policy-based configuration.
Solution Approach 2:
The patent changes the parameters of the planning process from manual iteration to automated computation. By transforming the planning approach from qualitative manual design to quantitative automated parameter determination based on statistical and machine learning models, the system achieves both customization accuracy and time efficiency.
4Adaptability or versatility
If manual provisioning and activation is performed, then flexibility in handling edge cases is maintained, but capital and operational expenditures increase
Solution Approach 1:
The automated provisioning system serves itself by automatically executing configuration deployment, resource allocation, and network activation without requiring manual intervention. The system handles edge cases through automated policy exceptions and adaptive configuration generation, maintaining flexibility while significantly reducing CAPEX and OPEX through elimination of manual labor.
Data Source
AI summary
This disclosure relates to method and system for legacy network transformation. The method includes receiving data corresponding to a legacy WAN from one or more data sources. Further, the method includes analyzing the data to identify one or more transformation requirements for the legacy WAN, based on one of a statistical technique, or a machine learning technique. Further, the method includes determining one or more configuration parameters corresponding to the legacy WAN based on a set of pre-defined business policy rules and the one or more transformation requirements. Further, the method includes generating a high-level design for SD-WAN based on the one or more transformation requirements and the one or more configuration parameters. Further, the method includes generating a low-level design including configuration guidelines templates for the SD-WAN based on the high-level design. Each configuration guidelines template may facilitate the transformation of legacy WAN to the SD-WAN.