SD-WAN Heat Map Analysis for Dynamic MFE Deployment
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Solution Overview
Problem
Current SD-WAN networks employ static policies for reliable and secure connectivity to cloud-based applications, leading to sub-optimal routing and degraded user experience due to the lack of consideration for evolving needs of next-generation cloud-native applications.
Innovation Solution
A method is introduced to generate a heat map based on metrics collected from managed forwarding elements (MFEs) in an SD-WAN, which accounts for data message flows, locations of MFEs, and destinations. This heat map is used to identify modifications to the SD-WAN to improve forwarding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static policies are used for SD-WAN routing, then reliability and security are improved, but forwarding efficiency and user experience deteriorate
Solution Approach 1:
The patent transforms static SD-WAN policies into dynamic policies that automatically adapt to changing network conditions. The system continuously collects metrics from multiple sources (MFEs, compute machines, destination locations) and uses heat map visualization to identify traffic patterns and bottlenecks in real-time, enabling the routing policy to dynamically optimize forwarding efficiency while maintaining security and reliability constraints.
Solution Approach 2:
The patent implements a feedback mechanism where network metrics are continuously collected from MFEs and compute machines, processed to generate heat maps showing traffic density and bottlenecks, and then used to automatically adjust routing policies. This closed-loop feedback system enables the SD-WAN to learn from network conditions and continuously improve forwarding efficiency while maintaining reliable and secure connectivity.
2Ease of manufacture
If services are statically provisioned close to sources, then deployment simplicity is improved, but performance and application quality deteriorate
Solution Approach 1:
The patent applies local quality by placing services and MFEs at specific geographic locations based on destination clusters identified through heat map analysis. Instead of uniform service placement, the system dynamically determines optimal locations for service provisioning based on where traffic density and bottlenecks are detected, ensuring services are positioned close to destinations rather than sources when performance requires it.
Solution Approach 2:
The patent introduces geographic dimensionality to service provisioning by using heat maps that visualize traffic patterns across different geographic locations. The system analyzes destination clusters and traffic flows across multiple geographic dimensions to determine optimal service placement locations, moving beyond simple source-proximity logic to multi-dimensional optimization that considers destination accessibility and traffic patterns.
Data Source
AI summary
Some embodiments provide a method for dynamically deploying a managed forwarding element (MFE) in a software-defined wide-area network (SD-WAN) for a particular geographic region across which multiple SaaS applications is distributed. The method determines, based on flow patterns for multiple flows destined for the multiple SaaS applications distributed across the particular geographic region, that an additional MFE is needed for the particular geographic region. The method configures the additional MFE to deploy at a particular location in the particular geographic region for forwarding the multiple flows to the multiple SaaS applications. The method provides, to a particular set of MFEs that connect a set of branch sites to the SD-WAN, a set of forwarding rules to direct the particular set of MFEs to use the additional MFE for forwarding subsequent data messages belonging to the multiple flows to the multiple SaaS applications.