Automated Subscriber Policy Adjustment via Network Condition Detection
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Solution Overview
Problem
Manual intervention by network operators in monitoring and adjusting subscriber policies is slow and inefficient, especially in complex networks with many parameters, leading to potential network congestion and poor user experiences due to the inability to react quickly to changing conditions.
Innovation Solution
An automated system comprising a servicing node and a policy management unit that applies traffic enforcement rules to data traffic, determines network conditions, and modifies attributes and rules based on attribute adjustment rules to optimize network performance without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention by network operators is used to monitor and adjust subscriber policies, then human operators can make decisions based on their expertise, but the reaction speed is slow and the system cannot respond quickly to changing network conditions
Solution Approach 1:
The system enables automated policy adjustment where the network management system itself performs monitoring, analysis, and policy modification without requiring human operator intervention. The servicing node automatically detects network conditions, determines policy changes needed, and implements adjustments, making the system self-sufficient in managing subscriber policies while maintaining expert-level decision accuracy through predefined rules and algorithms.
2Adaptability or versatility
If the number of monitored network parameters is increased to cover more aspects of network behavior, then the monitoring becomes more comprehensive, but the complexity of the monitoring task increases significantly
Solution Approach 1:
The monitoring system is divided into distinct functional components: a servicing node that collects and analyzes network parameters, a policy management unit that processes analysis results, and automated adjustment mechanisms. This segmentation allows comprehensive monitoring of multiple network aspects (traffic patterns, subscriber behavior, network performance) while distributing complexity across modular components, making the system manageable and scalable.
3Manufacturing precision
If policies are made more granular to control network behavior precisely, then the control precision improves, but the difficulty of monitoring and applying changes to policies increases
Solution Approach 1:
The policy management unit serves as an intermediary between the servicing node and the traffic enforcement rules. It receives analysis results from the servicing node, automatically determines appropriate policy changes based on predefined criteria, and applies granular adjustments to traffic enforcement rules. This intermediary automation eliminates the need for manual policy management while maintaining high control precision through rule-based decision-making.
4Productivity
If automated systems are used to adjust subscriber policies, then the reaction speed improves and the system can respond immediately to network conditions, but the complexity of the automated system increases
Solution Approach 1:
The system implements continuous feedback loops where the servicing node monitors network conditions, analyzes changes in real-time, and automatically adjusts subscriber policies based on detected conditions. The policy management unit receives feedback from network performance metrics and dynamically modifies traffic enforcement rules accordingly. This feedback mechanism enables rapid automated response to network events while managing complexity through event-driven architecture that only activates adjustments when conditions warrant changes.
Data Source
AI summary
Provided are methods and systems for adjusting subscriber policies. A method for adjusting of subscriber policies may include applying traffic enforcement rules to a data traffic associated with a subscriber. The method can further include determining network conditions associated with the data traffic. The method can include modifying, based on the determination of the network conditions, attributes according to attribute adjustment rules to obtain modified attributes. The method can further include modifying the traffic enforcement rules based on the modified attributes to obtain modified traffic enforcement rules.