Egress Point Selection via Network Tunnel Encapsulation
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Solution Overview
Problem
In large provider networks, existing routing protocols often select less efficient egress points for routing egress traffic due to limitations in accepting advertisements of less than 256 IP addresses, leading to suboptimal data packet forwarding.
Innovation Solution
A network system that identifies and selects an efficient egress point by retrieving an egress point identifier from a data source associated with the client connection's IP address or subnetwork, encapsulates data packets, and sends them through an instantiated network tunnel to the chosen egress point, allowing for optimized routing based on performance criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a default routing policy selects only one egress point based on standard routing protocols, then routing simplicity is maintained, but network performance and efficiency deteriorate due to suboptimal egress point selection
Solution Approach 1:
The routing policy is segmented into multiple components: standard routing protocols for basic functionality and a machine learning model for optimized egress point selection. This allows the system to maintain routing simplicity while incorporating advanced performance optimization without requiring complete redesign of the routing infrastructure.
Solution Approach 2:
A machine learning model acts as an intermediary between standard routing protocols and egress point selection. The model receives network state information, predicts optimal egress points, and provides recommendations that enhance routing performance without requiring changes to core routing protocol behavior.
2Measurement precision
If egress points advertise routes in blocks of 256 IP addresses, then routing advertisement simplicity is maintained, but egress point selection efficiency deteriorates due to inability to select specific IP addresses
Solution Approach 1:
The system adds a new dimension to routing advertisements by incorporating machine learning model predictions that operate at the individual IP address level, while the underlying routing infrastructure continues to use traditional block-based advertisements. This dimensional addition enables precise egress point selection without requiring complete overhaul of advertisement mechanisms.
Solution Approach 2:
The machine learning model changes the parameter of egress point selection from block-level (256 IP addresses) to individual IP address level by analyzing specific IP address characteristics and predicting optimal egress points, thereby achieving finer granularity in routing decisions.
3Productivity
If standard routing protocols are used for egress traffic routing, then routing protocol simplicity is maintained, but routing efficiency deteriorates due to inability to consider multiple performance criteria
Solution Approach 1:
The machine learning model serves multiple functions: it evaluates multiple performance criteria (latency, bandwidth, cost, usage), predicts optimal egress points, and integrates with existing routing protocols. This multi-functionality enables comprehensive routing optimization without requiring separate systems for each function.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously receives network state information, evaluates performance criteria, and adjusts egress point selections based on observed outcomes. This feedback loop enables the system to learn from past routing decisions and improve routing efficiency over time.
Data Source
AI summary
A method of identifying an egress point to a network location includes receiving a client connection from an external network (e.g., the internet) and retrieving an egress point identifier from an egress data source (e.g., a database, look-up table, hash table, data object etc.) for the received client connection. The egress data source stores egress point identifiers associated with at least one of an internet protocol address and a subnetwork. The method further includes binding the client connection to an egress point corresponding to the retrieved egress point identifier, encapsulating packets of data received from the client connection, and sending the encapsulated data packets through an instantiated network tunnel to the bound egress point. The method includes decapsulating the encapsulated data packets at least near the bound egress point. A default routing policy may then guide the data packets to the egress point.