Router Configuration Policy for Dynamic QoS Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for managing router configurations in 5G networks are inefficient and lack scalability, relying on human expertise and hardcoded rules, which are not adaptable to dynamic changes in traffic patterns, leading to sub-optimal Quality of Service (QoS) support and difficulty in diagnosing and addressing new network issues.
Innovation Solution
A computer-implemented method using reinforcement learning to generate and apply a policy for dynamically managing internal router configurations, based on performance data analysis and probabilistic modeling, allowing for automated adjustments to queue management and port configurations in response to changing network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human expert driven configuration methods are used, then QoS control is achievable, but scalability and adaptability to dynamic traffic patterns deteriorate
Solution Approach 1:
The router automatically monitors its own performance metrics (queue utilization, packet drop rates, latency) and self-adjusts configuration parameters without human intervention. The system uses reinforcement learning to autonomously determine optimal configuration changes based on real-time network conditions, enabling the device to serve and configure itself dynamically.
Solution Approach 2:
The configuration parameters of the router are transformed from static, manually-set values to dynamic variables that automatically adjust in response to changing network conditions. The system continuously learns from performance data and adapts queue management policies, port configurations, and resource allocation based on real-time traffic patterns and network state.
2Ease of manufacture
If hardcoded rules are used for router configuration, then initial QoS support is provided, but scalability and ability to handle new problems deteriorate
Solution Approach 1:
The patent replaces manual, rule-based configuration mechanisms with an automated machine learning system. Instead of relying on pre-programmed hardcoded rules that require human updates, the system uses reinforcement learning algorithms that automatically learn optimal configuration strategies from performance data, substituting mechanical rule-following with adaptive intelligent decision-making.
Solution Approach 2:
The system creates a digital model or representation of the network's operational state through performance monitoring, using this copied information to train and update configuration policies. The reinforcement learning agent learns from simulated and real performance data copies to generalize solutions across different network conditions and scenarios.
3Ease of operation
If manual configuration methods are used, then control over network resources is maintained, but real-time dynamic resource allocation deteriorates
Solution Approach 1:
The system implements continuous feedback loops where performance metrics (queue utilization, packet drop rates, latency measurements) are constantly monitored and fed back to the reinforcement learning agent. This feedback mechanism enables real-time adjustment of configuration parameters, allowing the router to respond dynamically to changing network conditions and allocate resources according to current demands rather than static manual settings.
Data Source
AI summary
A computer implemented method is disclosed for generating a policy for managing a configuration of internal components of a communication network node. The method includes obtaining performance data and generating a model of the communication network node The method further includes using the model to generate a first data set; and extracting, from the first data set: a set of conditional probabilities of operational state transition for the communication network node; and a set of conditional probabilities of changes in observed measure of performance for the communication network node. The method further comprises includes combining the extracted sets of conditional probabilities with a reward function to form a configuration model and generating a solution to the configuration model including a policy that is operable to propose a change in configuration of an internal component of the communication network node based on an observed measure of performance of the communication network node.