Traffic Detection Function Independent Policy Application
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Solution Overview
Problem
Current Traffic Detection Function (TDF) systems in 3GPP networks are dependent on the Policy and Charging Rules Function (PCRF) for application detection and reporting, leading to reduced resilience and accuracy in traffic management, especially during PCRF failures, and lack multi-dimensional application detection capabilities.
Innovation Solution
A method and system that enables the TDF to receive and process traffic monitoring conditions, determine packet properties, and associate them with multi-dimensional application identifiers, allowing for independent policy application and charging session management, even in the absence of PCRF, by using a control engine and processor to analyze packets and communicate policies directly, reducing dependencies and enhancing subscriber awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the TDF depends on the PCRF for application detection and reporting, then the system follows the standard 3GPP architecture, but the resilience and accuracy of traffic management are reduced, especially during PCRF failures
Solution Approach 1:
The TDF is enhanced with self-service capabilities to perform application detection and reporting independently without relying on the PCRF. The control engine within the TDF autonomously processes packets, determines application identifiers, and generates reports directly, enabling the system to function during PCRF failures and improving overall resilience.
Solution Approach 2:
The system separates the application detection and reporting functions from the PCRF by implementing a dedicated control engine within the TDF. This segmentation allows the TDF to operate independently for traffic detection while the PCRF handles policy enforcement, reducing the harmful dependency and improving reliability.
2Measurement precision
If the TDF uses traditional application detection methods, then the implementation is simpler, but the accuracy and multi-dimensional detection capability are insufficient
Solution Approach 1:
The system introduces multi-dimensional application detection by creating composite application identifiers that combine multiple detection dimensions (e.g., application type, device type, service type). This dimensional expansion enables more accurate and granular traffic classification while maintaining manageable complexity through structured identifier composition.
Solution Approach 2:
The control engine implements a universal detection mechanism that handles multiple detection dimensions and application types through a unified approach. The same control engine processes various packet properties, determines different types of application identifiers, and generates diverse reports, providing multi-functional capability without proportionally increasing complexity.
Data Source
AI summary
A method for managing traffic detection including: receiving predetermined traffic monitoring conditions; processing at least one packet to determine packet properties; determining an application identifier to associate with the traffic flow based on the packet properties; determining at least one policy to apply to the traffic flow based on the traffic monitoring conditions, packet properties and the application identifier; and communicating the at least one policy to be applied to the traffic flow. A system including: a traffic detection function (TDF) configured to receive predetermined traffic monitoring conditions, wherein the TDF includes: a processor configured to process at least one packet to determine packet properties; and a control engine configured to: determine an application identifier to associated with the flow; determine at least one policy to apply to the flow based on the traffic monitoring conditions, the packet properties and application identifier; and communicate the at least one policy.