Network Configuration Exploration for QoS Without Exhaustive Tuning

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Solution Overview

Problem

Existing network and system configurations face challenges in providing seamless connectivity and quality of service due to dynamic variables and the complexity of configuration parameters, especially in evolving communication networks like 5G, where reinforcement learning is impractical and risky, and there is a lack of understanding of potential configurations that may lead to performance degradation.

Innovation Solution

A method and apparatus for exploring configuration parameters and context attributes through clustering, bootstrapping, and learning to identify optimal settings by iteratively trialing a reduced set of values, leveraging principles of nearby performance impacts and context similarities, and using statistical methods to reduce the risk of degradation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If configuration parameters are extensively tuned using traditional methods, then quality of service may be improved, but the complexity of the tuning process increases and time consumption increases

Engineering Contradiction:

Improvequality of serviceVSAvoidtuning process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs self-configuration by automatically exploring configuration options and determining optimal settings without requiring extensive manual tuning by operators. The processing system autonomously evaluates configuration parameters based on observed service performance, eliminating the need for complex manual intervention while maintaining service quality.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system systematically varies configuration parameters to explore their impact on service performance. By changing parameters in a controlled manner and observing outcomes, the system identifies optimal configurations without requiring deep domain expertise or complex tuning processes, thus improving service quality while reducing operational complexity.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If all configuration values are trialed to ensure optimal performance, then quality of service is maximized, but the time required for configuration increases significantly

Engineering Contradiction:

Improvequality of serviceVSAvoidconfiguration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

Instead of trialing all possible configuration values, the system performs partial action by selecting and testing only a subset of configuration options. The processing system intelligently determines which configuration values to trial based on initial observations and performance metrics, achieving satisfactory optimization without the time cost of exhaustive testing of every possible value.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary exploration of configuration options before full deployment. By initially testing a reduced set of configuration values and observing performance trends, the system prepares optimal configurations in advance, avoiding the need for time-consuming exhaustive trials when actually deploying to production environments.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If configuration settings are changed to improve performance in one location, then service performance improves at that location, but it may cause performance degradation at other locations

Engineering Contradiction:

Improveservice performanceVSAvoidperformance degradation risk

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system applies local quality by determining configuration settings specific to each location's characteristics. The processing system observes service performance at different locations and tailors configuration values to local conditions, ensuring optimal performance at each location without causing degradation elsewhere. Each location receives customized configuration rather than uniform settings.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring service performance across multiple locations after configuration changes. When performance degradation is detected at any location, the system adjusts or reverts configuration settings accordingly. This feedback loop ensures that configuration optimizations at one location do not adversely affect other locations.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12556451B2Apparatuses and methods for enhancing quality of service via an exploration of configuration options

Publication Date: 2026.02.17 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Aspects of the subject disclosure may include, for example, establishing a first cluster comprising a first plurality of members, identifying a first plurality of values to trial in respect of a first member of the first plurality of members, the first plurality of values being included in a second plurality of values and being less than an entirety of the second plurality of values, performing a first plurality of trials in respect of the first member of the first plurality of members based on iterating amongst the first plurality of values to obtain a first plurality of results, selecting at least one value included in the first plurality of values based on the first plurality of results, and utilizing the at least one value for each member of the first plurality of members of the first cluster. Other embodiments are disclosed.