Dynamic Port Allocation in CG-NAT Networks

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Solution Overview

Problem

The scarcity of IPv4 addresses due to exponential Internet growth leads to inefficient static port allocations in CG-NAT architectures, resulting in unused ports and service quality issues as devices struggle with inadequate allocations, which can cause disruptions and poor user experience.

Innovation Solution

Implementing a method for dynamically allocating ports based on predicted needs using machine learning algorithms that analyze time series and historical data to reassess and reallocate ports between endpoint devices sharing a public IP address, allowing for efficient utilization of available ports and improving user experience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If static port allocations are used in CG-NAT architecture, then device complexity is reduced and ease of operation is improved, but port utilization efficiency deteriorates and service quality worsens due to unused ports and inadequate allocations

Engineering Contradiction:

Improveport utilization efficiencyVSAvoidport allocation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements dynamic port allocation by transitioning from static to dynamic port assignment in CG-NAT architecture. The system continuously monitors port usage metrics and automatically reallocates ports based on current network conditions and device needs, ensuring optimal port utilization while maintaining manageable system complexity through automated control mechanisms

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent employs feedback mechanisms by monitoring port usage metrics and utilizing machine learning algorithms to analyze historical data. This feedback loop enables the system to learn from past allocation patterns and continuously optimize port distribution, improving efficiency while the automated nature of the feedback process prevents excessive complexity increase

Inventive Principle:

Principle #23Feedback

2Reliability

If static port allocations are used, then system simplicity is maintained, but service quality deteriorates due to inadequate port allocations causing disruptions

Engineering Contradiction:

Improveservice qualityVSAvoidport allocation management simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent implements self-service mechanisms where the port allocation system automatically monitors its own performance metrics and performs reallocation without external intervention. The machine learning algorithms autonomously analyze usage patterns and adjust port assignments, improving service reliability while eliminating the need for complex manual management

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent applies preliminary action by using machine learning algorithms to predict future port allocation needs based on historical data and current trends. The system proactively reallocates ports before shortages or conflicts occur, preventing service disruptions and maintaining simplicity by automating the predictive process

Inventive Principle:

Principle #10Preliminary action

3Productivity

If dynamic port allocation with machine learning is implemented, then port utilization efficiency is improved and service quality enhanced, but device complexity increases due to additional algorithms and data processing

Engineering Contradiction:

Improveport utilization efficiencyVSAvoidport allocation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent achieves universality by designing a multi-functional port allocation system that combines monitoring, analysis, prediction, and reallocation capabilities in a single integrated framework. The machine learning model serves multiple purposes including anomaly detection, trend analysis, and optimization, improving efficiency without proportionally increasing complexity through functional consolidation

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If dynamic port allocation with machine learning is implemented, then service quality is enhanced by preventing disruptions, but device complexity increases due to additional processing requirements

Engineering Contradiction:

Improveservice qualityVSAvoiddata processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent substitutes mechanical manual port management with automated machine learning-based systems. The algorithms process allocation data and make reallocation decisions automatically, enhancing service reliability through consistent automated operation while reducing the complexity of manual intervention and human decision-making processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11909711B2Dynamic port allocations in carrier grade network address translation networks

Publication Date: 2024.02.20 AT&T INTELLECTUAL PROPERTY I L P

AI summary

An example method includes receiving a domain name server (DNS) query initiated by an endpoint device, determining a current port assignment for the endpoint device, changing an allocation of ports for the endpoint device from the current port assignment based on a predicted port allocation need for a current communication session associated with the domain name server query, and performing a network address translation in accordance with the allocation of ports for the endpoint device.