Overlay Network Edge Device Indirection Table for Fast Cutover
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Solution Overview
Problem
Traditional overlay networks face inefficiencies in traffic routing due to computationally intensive and resource-consuming MAC address learning processes, leading to communication delays and traffic loss during edge device failures, which limits scalability and causes non-deterministic convergence issues.
Innovation Solution
The implementation of an indirection table by edge devices in overlay networks, which maps site identifiers to responsible edge devices, allowing for faster traffic routing and reduced redundancy by delaying deletion of remote MAC addresses during edge device failures, enabling deterministic convergence and minimizing traffic loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional MAC address learning processes are used in overlay networks, then traffic routing can be established, but the process becomes computationally intensive and resource-consuming, leading to communication delays and traffic loss during edge device failures
Solution Approach 1:
The patent implements preliminary action by pre-establishing indirection tables that map site identifiers to edge device identifiers before failures occur. This allows immediate traffic redirection to backup edge devices without waiting for MAC address relearning, thus eliminating communication delays during edge device failures while maintaining routing reliability.
Solution Approach 2:
The patent extracts the MAC address learning process from the critical failure recovery path. By separating this computationally intensive process into a background task and implementing indirection tables for immediate failover, the system eliminates the delay caused by MAC address relearning during edge device failures.
2Loss of substance
If traditional overlay networks delay deletion of remote MAC addresses during edge device failures, then traffic loss is minimized, but the convergence becomes non-deterministic
Solution Approach 1:
The patent introduces feedback mechanisms through indirection tables that track which edge devices are responsible for which sites. This feedback allows deterministic convergence by providing clear state information about edge device roles, enabling the system to reliably determine the correct backup path without ambiguity, thus achieving both traffic loss minimization and convergence determinism.
3Measurement precision
If edge devices perform complete MAC address learning during failures, then routing accuracy is maintained, but scalability is limited due to resource consumption
Solution Approach 1:
The patent segments the routing function into two independent components: indirection tables for immediate failover and MAC address tables for accuracy. This segmentation allows the system to maintain routing accuracy through indirection table mappings while avoiding the resource-intensive complete MAC address relearning process, thereby improving network scalability.
Solution Approach 2:
The indirection table acts as an intermediary between the site identifier and the edge device identifier. This intermediary layer provides routing accuracy without requiring complete MAC address learning, as the indirection table translates site identifiers to the correct edge device, enabling scalability while maintaining precision.
Data Source
AI summary
Techniques are presented herein for optimizing traffic routing in overlay networks. At a first edge device located at a first site in a network, a message is received that indicates address information of a network device. The address information of the network device is stored in an address table. The address information is associated with a site identifier that identifies a second site at which the network device is located. The site identifier is mapped to an identifier associated with a second edge device that is responsible for routing traffic to network devices at the second site.